One of the problems that e-commerce users face is that the desired products are sometimes not available and Web shops fail to provide similar products due to their exclusive reliance on Boolean faceted search. User preferences are also often not taken into account. In order to address these problems, we present a novel framework specifically geared towards approximate faceted search within the product catalog of a Web shop. It is based on adaptations to the p-norm extended Boolean model, to account for the domain-specific characteristics of faceted search in an e-commerce environment. These e-commerce specific characteristics are, for example, the use of quantitative properties and the presence of user preferences. Our approach explores the concept of facet similarity functions in order to better match products to queries. In addition, the user preferences are used to assign importance weights to the query terms. Using a large-scale experimental setup based on real-world data, we conclude that the proposed algorithm outperforms the considered benchmark algorithms. Last, we have performed a user-based study in which we found that users who use our approach find more relevant products with less effort.

pyFUME is a python package for the automatic estimation of fuzzy inference systems. Fuzzy models are considered among the most interpretable, understandable, and transparent methods that are currently available, making them ideal for the development of Interpretable AI systems. Such models are suitable for the creation of decision support systems in extremely sensitive domains where the right to an explanation is particularly important, like medicine and healthcare. pyFUME can automatically estimate the antecedent sets and the consequent parameters of a Takagi-Sugeno fuzzy model directly from data, and deliver an executable fuzzy model implemented with the Simpful python library. The main limitation of pyFUME was that it was not well-equipped to deal with purely categorical, non-ordinal variables since it used distance metrics suitable for continuous variables to cluster the data for determining the fuzzy model’s structure. In this paper, we introduce a new version of pyFUME that supports mixed (i.e., continuous and categorical) data sets, relying on a novel version of fuzzy Cprototypes clustering. Our results show that our new approach is effective, leading to better fitting with respect to models based only on continuous features. We also present alternative plotting methods tailored for categorical variables, which improves the overall interpretability of the estimated discrete fuzzy sets.

Data transformation is an important step in Machine Learning pipelines which can strongly improve their performance. For instance, min-max normalization is often used to make all variables lie in the same range, while log-transformation is used to map data that is scattered across several orders of magnitude to a logarithmic space. Such transformations can be beneficial when the machine learning approach measures distance in a metric space, such as cluster-based approaches. These two transformation approaches can be combined to reveal hidden patterns in the data in the case of log-normally distributed data points, which commonly occur in biological and medical data. In this work we introduce a novel evolutionary approach designed to automatically determine the optimal log-transformation and selection of variables. Our approach is built around an interpretable AI system (created by pyFUME), so that all transformations are followed by inverse transformations to map back the values into the original universe of discourse, and preserve the interpretability of the results. We test our approach on two synthetic datasets, designed to reproduce a condition in which some variables are normally distributed, some variables are log-normally distributed, and some variables are just noise in the dataset. Our results show that our approach yields better performing models compared to conventional methods, and that the resulting model is also characterised by a better interpretability, making such approach particularly useful to study biomedical datasets.

Nowadays, the growing amounts of collected data enable the training of machine learning models that can be used to extract insights from the data and make better-informed decisions. Among the possible models that can be learned from data are fuzzy rule-based models, which are transparent and enable – when properly designed – interpretable artificial intelligence. One of the requirements of interpretability is a simple model structure, which can be achieved by performing feature selection and by limiting the number of rules in the model. However, the chosen feature set and the number of rules may interact and strongly affect the model’s accuracy. In this study, we employ techniques from the field of evolutionary computation to perform feature and rule number selection simultaneously. To ensure the developed models do not only perform well but are also interpretable and have good generalization capabilities, we adopt a multi-objective approach in which we train the models focusing on three objectives: performance, complexity, and model stability. In this way, we strive to develop simple, well-performing parsimonious fuzzy models. We show the effectiveness of our approach on three benchmark data sets.

Unstructured text data is collected daily in large amounts by many organizations. Analyzing all this data is time intensive and too costly in many cases. One technique to systematically analyze large corpora of texts is topic modeling, which returns the latent topics present in a corpus. Recently, several fuzzy topic modeling algorithms have been proposed and have shown superior results over the existing algorithms. Although various Python libraries offer topic modeling algorithms, none includes fuzzy topic models. Therefore, we present FuzzyTM, a Python library for training fuzzy topic models and creating topic embeddings for downstream tasks. The user-friendly pipelines with default values allow practitioners to train a topic model with minimal effort. Meanwhile, its modular design allows researchers to modify each software element and for future methods to be added.

The written notes in the Electronic Health Records contain a vast amount of information about patients. Implementing automated approaches for text classification tasks requires the automated methods to be well-interpretable, and topic models can be used for this goal as they can indicate what topics in a text are relevant to making a decision. We propose a new topic modeling algorithm, FLSA-E, and compare it with another state-of-the-art algorithm FLSA-W. In FLSA-E, topics are found by fuzzy clustering in a word embedding space. Since we use word embeddings as the basis for our clustering, we extend our evaluation with word-embeddings-based evaluation metrics. We find that different evaluation metrics favour different algorithms. Based on the results, there is evidence that FLSA-E has fewer outliers in its topics, a desirable property, given that within-topic words need to be semantically related.

In this work we consider a seller who sells an item via second-price auctions with a reserve price. By controlling the reserve price, the seller can influence the revenue from the auction, and in this paper, we propose a method for learning optimal reserve prices. We study a limited information setting where the probability distribution of the bids from bidders is unknown and the values of the bids are not revealed to the seller. Furthermore, we do not assume that the seller has access to a historical data set with bids. Our main contribution is a method that incorporates knowledge about the rules of second-price auctions into a multiarmed bandit framework for optimizing reserve prices in our limited information setting. The proposed method can be applied in both stationary and nonstationary environments. Experiments show that the proposed method outperforms state-of-the-art bandit algorithms. In stationary environments, our method outperforms these algorithms when the horizon is short and performs as good as they do for longer horizons. Our method is especially useful if there is a high number of potential reserve prices. In addition, our method adapts quickly to changing environments and outperforms state-of-the-art bandit algorithms designed for nonstationary environments. Summary of Contribution: A key challenge in online advertising is the pricing of advertisements in online auctions. The scope of our study is second-price auctions with a focus on the reserve price optimization problem from a seller’s point of view. This problem is motivated by the real-life practice of small and medium-sized web publishers. However, the proposed solution approach is applicable to any seller who sells an item via second-price auctions and wants to optimize its reserve price during these auctions. Our solution approach is based on techniques from machine learning and operations research, and it would be beneficial especially for sellers who start the selling process without any historical data and can collect the data on the outcomes of the auctions while making reserve price decisions over time. History: Accepted by RamRamesh, Area Editor for Data Science & Machine Learning. Supplemental Material: The supplementary material is available at https://doi.org/10.1287/ijoc.2022.1199 .

Real Time Bidding is the process of selling and buying online advertisements in real time auctions. Real time auctions are performed in header bidding partners or ad exchanges to sell publishers' ad placements. Ad exchanges run second price auctions and a reserve price should be set for each ad placement or impression. This reserve price is normally determined by the bids of header bidding partners. However, ad exchange may outbid higher reserve prices and optimizing this value largely affects the revenue. In this paper, we propose a deep reinforcement learning approach for adjusting the reserve price of individual impressions using contextual information. Normally, ad exchanges do not return any information about the auction except the sold-unsold status. This binary feedback is not suitable for maximizing the revenue because it contains no explicit information about the revenue. In order to enrich the reward function, we develop a novel reward shaping approach to provide informative reward signal for the reinforcement learning agent. Based on this approach, different intervals of reserve price get different weights and the reward value of each interval is learned through a search procedure. Using a simulator, we test our method on a set of impressions. Results show superior performance of our proposed method in terms of revenue compared with the baselines.

Deep Reinforcement Learning (RL) has achieved high success in solving routing problems. However, state-of-the-art deep RL approaches require a considerable amount of data before they reach reasonable performance. This may be acceptable for small problems, but as instances grow bigger, this fact severely limits the applicability of these methods to many real-world instances. In this work, we study a setting where the agent can access data from previously handcrafted heuristics for the Traveling Salesman Problem. In our setting, the agent has access to demonstrations from 2-opt improvement policies. Our goal is to learn policies that can surpass the quality of the demonstrations while requiring fewer samples than pure RL. In this study, we propose to first learn policies with Imitation Learning (IL), leveraging a small set of demonstration data to accelerate policy learning. Afterward, we combine on policy and value approximation updates to improve performance over the expert's performance. We show that our method learns good policies in a shorter time and using less data than classical policy gradient, which does not incorporate demonstration data into RL. Moreover, in terms of solution quality, it performs similarly to other state-of-the-art deep RL approaches.

BACKGROUND: Tremor severity assessment is an important step for the diagnosis and treatment decision-making of essential tremor (ET) patients. Traditionally, tremor severity is assessed by using questionnaires (e.g., ETRS and QUEST surveys). In this work we assume the possibility of assessing tremor severity using sensor data and computerized analyses. The goal of this work is to assess severity of tremor objectively, to be better able to asses improvement in ET patients due to deep brain stimulation or other treatments. METHODS: We collect tremor data by strapping smartphones to the wrists of ET patients. The resulting raw sensor data is then pre-processed to remove any artifact due to patient's intentional movement. Finally, this data is exploited to automatically build a transparent, interpretable, and succinct fuzzy model for the severity assessment of ET. For this purpose, we exploit pyFUME, a tool for the data-driven estimation of fuzzy models. It leverages the FST-PSO swarm intelligence meta-heuristic to identify optimal clusters in data, reducing the possibility of a premature convergence in local minima which would result in a sub-optimal model. pyFUME was also combined with GRABS, a novel methodology for the automatic simplification of fuzzy rules. RESULTS: Our model is able to assess tremor severity of patients suffering from Essential Tremor, notably without the need for subjective questionnaires nor interviews. The fuzzy model improves the mean absolute error (MAE) metric by 78-81% compared to linear models and by 71-74% compared to a model based on decision trees. CONCLUSION: This study confirms that tremor data gathered using the smartphones is useful for the constructing of machine learning models that can be used to support the diagnosis and monitoring of patients who suffer from Essential Tremor. The model produced by our methodology is easy to inspect and, notably, characterized by a lower error with respect to approaches based on linear models or decision trees.

In many business processes, knowledge workers collect information and make decisions about business entities. Artifact-centric process (ACP) models have been proposed to represent such knowledge-intensive processes. Declarative ACP models use precise business rules to define flexible process executions. However, in many business situations knowledge experts have to deal with uncertainty and vagueness. Currently, how to deal with such situations cannot be expressed in declarative ACP models. We propose to use the fuzzy logic framework to model uncertainty in such models. Using Guard-Stage-Milestone (GSM) schemas as declarative ACP notation, we show how GSM schemas can be adapted for modeling gradual progress of real-life knowledge-intensive processes via a fuzzy logic interface. We evaluate the proposed fuzzy GSM schemas in real-life scenarios of airport ground operations and healthcare services.

This paper proposes a Deep Reinforcement Learning (DRL) approach for solving knapsack problem. The proposed method consists of a state aggregation step based on tabular reinforcement learning to extract features and construct states. The state aggregation policy is applied to each problem instance of the knapsack problem, which is used with Advantage Actor Critic (A2C) algorithm to train a policy through which the items are sequentially selected at each time step. The method is a constructive solution approach and the process of selecting items is repeated until the final solution is obtained. The experiments show that our approach provides close to optimal solutions for all tested instances, outperforms the greedy algorithm, and is able to handle larger instances and more flexible than an existing DRL approach. In addition, the results demonstrate that the proposed model with the state aggregation strategy not only gives better solutions but also learns in less timesteps, than the one without state aggregation.

Living in the era of "data deluge" demands for an increase in the application and development of machine learning methods, both in basic and applied research. Among these methods, in the last decades fuzzy inference systems carved out their own niche as (light) grey box models, which are considered more interpretable and transparent than other commonly employed methods, such as artificial neural networks. Although commercially distributed alternatives are available, software able to assist practitioners and researchers in each step of the estimation of a fuzzy model from data are still limited in scope and applicability. This is especially true when looking at software developed in Python, a programming language that quickly gained popularity among data scientists and it is often considered their language of choice. To fill this gap, we introduce pyFUME, a Python library for automatically estimating fuzzy models from data. pyFUME contains a set of classes and methods to estimate the antecedent sets and the consequent parameters of a Takagi-Sugeno fuzzy model from data, and then create an executable fuzzy model exploiting the Simpful library. pyFUME can be beneficial to practitioners, thanks to its pre-implemented and user-friendly pipelines, but also to researchers that want to fine-tune each step of the estimation process.

The number of bags mishandled while transferring to a connecting flight is high. Bags at-risk of missing their connections can be processed faster; however, identifying such bags at-risk is still done by simple business rules. This work researches a general model of baggage transfer process and proposes a complex prediction model for identifying the bags at-risk. Our prediction model is compared to the current rule based method and a benchmark using logistic regression. The results show that our model offers an increase in accuracy coupled with a marked increase in precision and recall when identifying bags that are transferred unsuccessfully.

The extremely high turnover of online advertising makes it one of the most important sources of income for many online ad publishers. Advertising through world wide web is mainly performed by Real Time Bidding in which the advertisers and the publishers participate to online auctions for trading the ad slots. Publishers usually set the reserve prices for their ad slots and any winning buyer in the auctions performed by ad exchanges has to pay at least the value of reserve price. Header bidding is a way of real time bidding and it becomes very popular, but how to use it together with advertising Exchanges (AdX) to achieve good revenue for online publishers is not well studied. In this paper, we propose a method that makes use of the historical auction data from header bidding and AdX to learn and optimize the reserve price for AdX. We propose a method based on supervised learning and survival analysis to increase the reserve price. The method assumes no information about current auctions and the bids of header bidding and AdX response are predicted and used to determine the highest possible reserve price. The experiments with real-world auction data show the promising results of our method in increasing the expected revenue of online publishers.

The papers in this special section focus on recent developments and emerging topics in the area of deep fuzzy models that address some of the problems and limitations above. These models have been known under different names, such as hierarchical fuzzy systems and fuzzy networks. They are usually well suited for performing multiple functional compositions at either crisp or linguistic level. Deep learning has gained significant attention within the computational intelligence community in recent years. Its success has been mainly due to the increased power of modern computational platforms in terms of their ability to collect, store, and process large volumes of data. This has led to a substantial increase in the effectiveness and efficiency of data management. As a result, it has become possible to achieve high accuracy for some benchmark learning tasks, such as object classification and image recognition within a short time frame. The most common implementation of deep learning has been through neural networks due to the ability of their layers of neurons to perform multiple functional compositions as part of a multistage learning process.

One of the main mechanisms that online publishers use in online advertising in order to sell their advertisement space is the real-time bidding (RTB) mechanism. In RTB the publisher sells advertisement space via a second-price auction. Publishers can set a reserve price for their inventory in the second-price auction. In this paper we consider an online publisher that sells advertisement space and propose a method for learning optimal reserve prices in second-price auctions. We study a limited information setting where the values of the bids are not revealed and no historical information about the values of the bids is available. Our proposed method leverages the dynamics of particles in particle swarm optimization (PSO) to set reserve prices and is suitable for non-stationary environments. We also show that, taking the gap between the winning bid and second highest bid into account leads to better decisions for the reserve prices. Experiments using real-life ad auction data show that the proposed method outperforms popular bandit algorithms.

Online advertising is one of the most important sources of income for many online publishers. The process is as easy as placing slots in the website and selling those slots in real time bidding auctions. Since websites load in few milliseconds, the bidding and selling process should not take too much time. Sellers or publishers of advertisements aim to maximize the revenue obtained through online advertising. In this paper, we propose a method to select the most profitable ad network for each ad request that is built upon our previous work [1]. The proposed method consists of two parts: a prediction model and a reinforcement learning modeling. We test two strategies of selecting ad network orderings. The first strategy uses the developed prediction model to greedily choose the network with the highest expected revenue. The second strategy is a two-step approach, where a reinforcement learning method is used to improve the revenue estimation of the prediction model. Using real AD auction data, we show that the ad network ordering obtained from the second strategy returns much higher revenue than the first strategy.

In this paper we consider an online publisher that sells advertisement space and propose a method for learning optimal reserve prices in second-price auctions. We study a limited information setting where the values of the bids are not revealed and no historical information about the values of the bids is available. Our proposed method is based on the principle of Thompson sampling combined with a particle filter to approximate and sample from the posterior distribution. Our method is suitable for non-stationary environments, and we show that, when the distribution of the winning bid suffers from estimation uncertainty, taking the gap between the winning bid and second highest bid into account leads to better decisions for the reserve prices. Experiments using real-life ad auction data show that the proposed method outperforms popular bandit algorithms.

Clustering analysis is an important computational task that has applications in many domains. One of the most popular algorithms to solve the clustering problem is fuzzy c-means, which exploits notions from fuzzy logic to provide a smooth partitioning of the data into classes, allowing the possibility of multiple membership for each data sample. The fuzzy c-means algorithm is based on the optimization of a partitioning function, which minimizes inter-cluster similarity. This optimization problem is known to be NP-hard and it is generally tackled using a hill climbing method, a local optimizer that provides acceptable but sub-optimal solutions, since it is sensitive to initialization and tends to get stuck in local optima. In this work we propose an alternative approach based on the swarm intelligence global optimization method Fuzzy Self-Tuning Particle Swarm Optimization (FST-PSO). We solve the fuzzy clustering task by optimizing fuzzy c-means' partitioning function using FST-PSO. We show that this population-based metaheuristics is more effective than hill climbing, providing high quality solutions with the cost of an additional computational complexity. It is noteworthy that, since this particle swarm optimization algorithm is self-tuning, the user does not have to specify additional hyperparameters for the optimization process.

In this paper we consider an online publisher that sells advertisement space and propose a method for learning optimal reserve prices in second-price auctions. We study a limited information setting where the values of the bids are not revealed and no historical information about the values of the bids is available. Our proposed method combines an adaptive search procedure with a fuzzy logic pricing step to set reserve prices and is suitable for non-stationary environments. In the fuzzy logic pricing step, we take the gap between the winning bid and second highest bid into account and show that this leads to better decisions for the reserve prices. Experiments using real-life ad auction data show that the proposed method outperforms popular bandit algorithms.

A high percentage of online advertising is currently performed through real time bidding. Impressions are generated once a user visits the websites containing empty ad slots, which are subsequently sold in an online ad exchange market. Nowadays, one of the most important sources of income for publishers who own websites is through online advertising. From a publisher’s point of view it is critical to send its impressions to most profitable ad networks and to fill its ad slots quickly in order to increase their revenue. In this paper we present a method for helping publishers to decide which ad networks to use for each available impression. Our proposed method uses reinforcement learning with initial state-action values obtained from a prediction model to find the best ordering of ad networks in the waterfall fashion. We show that this method increases the expected revenue of the publisher.

In this work we study a dynamic pricing problem with demand censoring and limited price changes. In our problem there is a seller of a single product that aims to maximize revenue over a finite sales horizon. The seller does not know the form of the mean demand function but does have some limited knowledge. We assume that the seller has a hypothesis set of mean demand functions and that the true mean demand function is an element of this set. Furthermore, the seller faces a business constraint on the number of price changes that is allowed during the sales horizon. More specifically, the number of price changes that the seller is allowed to make is bounded above by a finite integer. We furthermore assume that the seller can only observe the sales (minimum between realized demand and available inventory) and thus that demand is censored. In each period the seller can replenish his inventory to a particular level. The objective of the seller is to set the best price and inventory level in each period of the sales horizon in order to maximize his profit. The profit is determined by the revenue of the sales minus holding costs and costs for lost sales (unsatisfied demand). In determining the best price and inventory level the seller faces and exploration-exploitation trade-off. The seller has to experiment with different prices and inventory levels in order to learn from historical sales data which contains information about market responses to offered prices. On the other hand, the seller also needs to exploit what it has learned and set prices and inventory levels that are optimal given the information collected so far. We propose a heuristic policy for this problem and study its performance using numerical experiments. The results are promising and indicate that the growth rate of regret of the policy is sub-linear with respect to the sales horizon.

The expected post-operatory weight loss is not always achieved after bariatric surgery. Efforts have been done to describe the causes. Recently, total weight loss (%TWL) has been pointed out to better assess weight loss in bariatric patients. However, there is no cut off point that delimits the patients who successfully achieve their weight goals after a bariatric surgery. In this work, a method based on fuzzy modeling is implemented to help clinicians setting up the best cut-off point in %TWL for a specific population. The best boundary to delimit success and failure will be selected based on the predictive performance of the assessed cut-off points: 25, 30, 35 and 40%TWL after one and two years of surgery. Area under the receiver operating characteristic curve (AUC) values of 0.70 and 0.75 were achieved for the first and second post-surgery periods, respectively. Further, features not previously described as predictors of weight loss were identified as good predictors of the outcome.

Many fuzzy inference systems are built estimating their parameters from data. In particular, Takagi-Sugeno systems have been used a lot in data-driven fuzzy modeling. In this paper, we investigate one step in the data-driven identification of these models, namely the antecedent estimation when fuzzy clustering is used for estimating antecedent memberships and fuzzy rules. We propose removing noise coming from cluster membership values to obtain more specific antecedent sets, which is important for the interpretability of the models. The results obtained and presented in this paper show that this additional step leads to improved performance of the fuzzy model and higher specificity of the antecedent sets.

A popular method in machine learning for supervised classification is a decision tree. In this work we propose a new framework to learn fuzzy decision trees using mathematical programming. More specifically, we encode the problem of constructing fuzzy decision trees using a Mixed Integer Linear Programming (MIP) model, which can be solved by any optimization solver. We compare the performance of our method with the performance of off-the-shelf decision tree algorithm CART and Fuzzy Inference Systems (FIS) using benchmark data-sets. Our initial results are promising and show the advantages of using non-crisp boundaries for improving classification accuracy on testing data.

Currently many webshops rely on a fixed list of product facets to help users find the products of interest. Such a solution suffers from two problems: (1) it is difficult to devise a fixed list of facets that would satisfy all user interests, and (2) the top facets could become obsolete when all product results have these facets. To address these two problems we propose a novel algorithm for dynamic ordering of the product facets based on the query results. This algorithm relies on measures such as specificity and dispersion for qualitative and quantitative facets, respectively, to rank the properties associated with these facets so that users are able to find the products of interest with a minimum number of drill-down steps. Using a large-scale simulation study and a user-based evaluation, we show that our algorithm outperforms the expert-based fixed facets approach, a greedy baseline, and a state-of-the-art entropy-based solution. This paper is an extended abstract of our previous work [1].

In recent years, it has been demonstrated that checklists can improve patient safety significantly. To facilitate the effective use of checklists in daily practice, both the medical community and the informatics community propose to implement checklists in dynamic checklist applications that can be integrated into the clinical workflow and that is specific to the patient context. However, it is difficult to develop such applications because they are tightly intertwined with the content of specific checklists. We propose a platform that enables access to dynamic checklist applications by configuring the infrastructures provided in the platform. Then, the applications can be developed without time-consuming programming work. We define a number of design criteria regarding point of care and clinical processes by analyzing the existing checklist applications and the lessons learned from implementations. Then, by applying rule-based clinical decision support and workflow management technologies, we design technical mechanisms to satisfy the design criteria. A dynamic checklist application platform is designed based on these mechanisms. Finally, we build a platform in various design cycle iterations, driven by multiple clinical cases. By applying the platform, we develop nine comprehensive dynamic checklist applications with 242 dynamic checklists. The results demonstrate both the feasibility and the overall generic nature of the proposed approach. We propose a novel platform for configuring dynamic checklist applications. This platform satisfies the general requirements and can be easily configured to satisfy different scenarios in which safety checklists are used.

Clinical pathways (CPs) are popular healthcare management tools to standardize care and ensure quality. Analyzing CP compliance levels and variances is known to be useful for training and CP redesign purposes. Flexible semantics of the business process model and notation (BPMN) language has been shown to be useful for the modeling and analysis of complex protocols. However, in practical cases one may want to exploit that CPs often have the form of task-time matrices. This paper presents a new method parsing complex BPMN models and aligning traces to the models heuristically. A case study on variance analysis is undertaken, where a CP from the practice and two large sets of patients data from an electronic medical record (EMR) database are used. The results demonstrate that automated variance analysis between BPMN task-time models and real-life EMR data are feasible, whereas that was not the case for the existing analysis techniques. We also provide meaningful insights for further improvement.

Vitamin B12 deficiency is a common disorder with severe impacts on hematological and neurological disorders. Identifying vitamin B12 deficiency is not straightforward since blood vitamin B12 levels are not representative for actual vitamin B12 status in tissue. Instead, methylmalonic acid (MMA) levels in the plasma are used as indicators of vitamin B12 deficiency. MMA concentrations increase starting from the early course of vitamin B12 deficiency but they may also be high regardless of vitamin B12 deficiency due to renal failure (measured by eGFR). In this paper we propose the use of probabilistic fuzzy systems (PFS) to explore the relationship between MMA plasma levels with vitamin B12 and kidney function. We propose a PFS model for the analysis of overall MMA properties for all patients and also specific MMA properties for individual patients. We show that this PFS model leads to accurate MMA interval predictions. We further show that the proposed model can be used to assess a change in the eGFR level to a normal eGFR level, and its effect on the patient's MMA distribution.

Generation of linguistic summaries that are compact, short and relevant to the user remains an open challenge. In this paper, we propose a novel method for improving the generation of linguistic summaries inspired by the a-priori algorithm and the degree of appropriateness. The method generates all true summaries with related predicates in the summarizer, resulting in a small set of linguistic summaries, whose presentation to the user is compact. We tested our method on three real world data sets. The results indicate that our proposed approach is a good alternative to previous methods suggested for generating linguistic summaries.

In task allocation problems one usually only considers a single round in which players participate. In practice, many allocation problems are of repeated nature, in which players can decide to keep participating or leave. Players' participation, or behavior, influences the outcome, or social welfare, of these problems. In this paper, we use a fuzzy connective to model agents' behavior in regard to their perception of the game, i.e., optimism level, based on their experiences thus far. We conduct simulations to investigate the interactions between the agents' participation behaviors and the outcomes of the task allocations in multiple rounds. We compare two task allocation algorithms, one merely focusing on costs, and the other focusing on both fairness in the allocation and costs. The results show that the fairer algorithm makes agents more optimistic, and in return, agents keep participating in the allocation game. This leads to a higher social welfare in the long run compared to the cost-minimization algorithm.

Faceted browsing is widely used in Web shops and product comparison sites. In these cases, a fixed ordered list of facets is often employed. This approach suffers from two main issues. First, one needs to invest a significant amount of time to devise an effective list. Second, with a fixed list of facets, it can happen that a facet becomes useless if all products that match the query are associated to that particular facet. In this work, we present a framework for dynamic facet ordering in e-commerce. Based on measures for specificity and dispersion of facet values, the fully automated algorithm ranks those properties and facets on top that lead to a quick drill-down for any possible target product. In contrast to existing solutions, the framework addresses e-commerce specific aspects, such as the possibility of multiple clicks, the grouping of facets by their corresponding properties, and the abundance of numeric facets. In a large-scale simulation and user study, our approach was, in general, favorably compared to a facet list created by domain experts, a greedy approach as baseline, and a state-of-the-art entropy-based solution.

Probabilistic fuzzy systems (PFS) are shown to be valuable methods for conditional density estimation that combine fuzziness or linguistic uncertainty and probabilistic uncertainty. Several PFS applications have shown the added value of the different reasoning mechanisms of PFS and gains from incorporating two types of uncertainty. The effects of parametrization and parameter estimation on the function or conditional density approximations of PFS have not been documented in the literature. This paper aims to fill this gap in the literature by analyzing the parameters of PFS in conditional density estimation and point forecast using synthetic and real data applications. We show that both in-sample and out-of-sample results depend on PFS parametrization and the results deteriorate when the probability parameters of PFS are not optimized correctly, since these parameters allow the system to be fine tuned.

Two new methods for the optimization of probabilistic fuzzy classifiers are proposed. Probabilistic fuzzy systems are specially attractive due to their explicit and simultaneous modelling of two kinds of uncertainty, namely vagueness in linguistic terms (fuzziness) and probabilistic uncertainty. The current method uses the maximization of the likelihood with the stochastic gradient descent, which not only converges to local minima but also does not guarantee the minimization of the misclassification error. The proposed methods address this specific problem by incorporating global search techniques. The first algorithm proposed is a genetic algorithm with simple crossover and mutation operations. The other is a first generation memetic algorithm which combines the genetic algorithm with the stochastic gradient descent. A total of five benchmarks were used to compare the three algorithms. The results show that the proposed methods have an average relative improvement of 2% and 6% for the accuracy with the genetic and memetic algorithms, respectively.

Clinical safety checklists receive much research attention since they can reduce medical errors and improve patient safety. Computerized checklist support systems are also being developed actively. Such systems should individualize checklists based on information from the patient’s medical record while also considering the context of the clinical workflows. Unfortunately, the form definitions, database queries and workflow definitions related to dynamic checklists are too often hard-coded in the source code of the support systems. This increases the cognitive effort for the clinical stakeholders in the design process, it complicates the sharing of dynamic checklist definitions as well as the interoperability with other information systems. In this paper, we address these issues by contributing the DCCSS meta-model which enables the model-based development of dynamic checklist support systems. DCCSS was designed as an incremental extension of standard meta-models, which enables the reuse of generic model editors in a novel setting. In particular, DCCSS integrates the Business Process Model and Notation (BPMN) and the Guideline Interchange Format (GLIF), which represent best of breed languages for clinical workflow modeling and clinical rule modeling respectively. We also demonstrate one of the use cases where DCCSS has already been applied in a clinical setting.

It has recently been demonstrated that checklists can enable significant improvements to patient safety. However, their clinical acceptance is significantly lower than expected. This is due to the lack of good support systems. Specifically, support systems are too static: this holds for paper-based support as well as for electronic systems that digitize paper-based support naively. Both approaches are independent from clinical process and clinical context. In this paper, we propose a process-oriented and context-aware dynamic checklist support system: Trace book. This system supports the execution of complex clinical processes and rules involving data from Electronic Medical Record systems. Workflow activities and forms are specific to individual patients based on clinical rules and they are dispatched to the right user automatically based on a process model. Besides describing the Trace book functionality in general, this paper demonstrates the support system specifically on an example application that we are preparing for a controlled clinical evaluation. At last we discuss the limitations of Trace book.

Probabilistic fuzzy systems (PFS), a model which combines a linguistic description of the system behaviour with statistical properties of data, have been successfully applied to one day ahead Value at Risk (VaR) estimation for the stock market returns data. In this work, we propose a multi-covariate multi-output PFS model which provides the conditional density forecasts of returns for one day ahead and one month ahead periods. Such a multi-output PFS model was not considered in the literature. Furthermore, this model allows to analyze seasonal patterns in returns. The proposed model is applied to daily S&P500 stock returns. It is found that the proposed model indicates seasonal patterns in short and longer horizons as well as conservative VaR in long term forecasts. The model is shown to perform well in VaR estimation according to the unconditional coverage and independence tests.

In this paper we present a framework for automatic exploitation of news in stock trading strategies. Events are extracted from news messages presented in free text without annotations. We test the introduced framework by deriving trading strategies based on technical indicators and impacts of the extracted events. The strategies take the form of rules that combine technical trading indicators with a news variable, and are revealed through the use of genetic programming. We find that the news variable is often included in the optimal trading rules, indicating the added value of news for predictive purposes and validating our proposed framework for automatically incorporating news in stock trading strategies.

Clinical protocols are a popular instrument to document how clinicians are expected to behave under specific conditions. Protocols are typically based on internationally peer reviewed clinical guidelines as well as on hospital-local agreements. Existing techniques for monitoring protocol adherence only support protocol descriptions involving simple sequences and local decision rules. As care and cure processes are becoming increasingly complex, the need for more advanced techniques naturally emerges. In this paper we present a novel approach to defining and monitoring complex clinical protocols. By using BPMN to document protocols we enable the concise specification of protocols that involve multiple stakeholders that operate in parallel and under uncertainty. Uncertainty relates to the fact that protocols may involve complex loops and choices. While this specification style was becoming increasingly popular in the literature and practice of hospital management and operations management in general, corresponding conformance analysis techniques were still lacking. This paper contributes the first such technique and evaluate it on a complex compliance pattern from the cardiology domain.

Multifaceted search is a commonly used interaction paradigm in e-commerce applications, such as Web shops. Because of the large amount of possible product attributes, Web shops usually make use of static information to determine which facets should be displayed. Unfortunately, this approach does not take into account the user query, leading to a non-optimal facet drill down process. In this paper, we focus on automatic facet selection, with the goal of minimizing the number of steps needed to find the desired product. We propose several algorithms for facet selection, which we evaluate against the state-of-the-art algorithms from the literature. We implement our approach in a Web application called faccy.net. The evaluation is based on simulations employing 1000 queries, 980 products, 487 facets, and three drill down strategies. As evaluation metrics we use the average number of clicks, the average utility, and the top-10 promotion percentage. The results show that the Probabilistic Entropy algorithm significantly outperforms the other considered algorithms.

Ontology alignment systems are software tools aimed at producing a set of correspondences, called alignment, between two heterogeneous ontologies in order to bring them in a mutual agreement. Performing this task is an essential step to allow the exchange of information between people, organizations and web applications using ontologies for representing their view of the world. Currently, in spite of several ontology alignment systems have been developed, there is no a robust solution that seems capable of producing alignments with the same high quality on different alignment task instances. Mainly, this weakness of ontology alignment systems is due to the dependence of their behavior on a set of specific instance parameters. This work proposes to improve performance of a well-known memetic algorithm based ontology alignment system by adaptively regulating its specific instance parameters through a FML-based fuzzy tuning. The validity of our proposal is shown by aligning ontologies belonging to two well-known OAEI datasets and by performing a Wilcoxon's signed rank test which highlights that our proposal statistically outperforms its not fuzzy adaptive counterpart.

Linguistic summarization is a data mining and knowledge discovery approach to extract patterns and sum up large volume of data into simple sentences. There is a large research in generating linguistic summaries which can be used to better understand and communicate about patterns, evolution and long trends in numerical, time series or labelled data. The objective of this work is to develop a computational system capable of automatically generating linguistic descriptions of time series data of septic shock patients containing labelled data, not only of the whole series, but also on the differences between subsets of the data. This is of particular interest in septic shock, as the differences between patients are not well understood. For this purpose we propose a new type of differential summaries, based on a numerical criterion assessing the characteristics of the summary on each subset of interest. Furthermore, this paper proposes an extension of linguistic summaries to provide temporal and categorical contextualization. This is of particular interest in healthcare to detect differences related to a condition or illness as well as the effectiveness of the administered treatment.

We propose the application of probabilistic fuzzy systems (PFS) to model the prediction of early readmission in intensive care unit patients and compare it with the gold-standard method - logistic regression based on the APACHE II score. PFS are characterized by the combination of the linguistic description of the system with the statistical properties of data. On one hand, results point that PFS models perform comparably to the gold-standard method, with AUC values of 0.66±0.03. On the other hand, results also show that PFS models use a significant lower number of variables which, from the clinical practice point of view, suggests improved gains in terms of simplicity.

Achieving semantic interoperability is an essential task for all distributed and open knowledge based systems. Currently, the best technology recognized for fulfilling this complex task is represented by ontologies. Unfortunately, in turn, the power of ontological representation is reduced by the semantic heterogeneity problem which affects two ontologies when they are characterized by terminological and conceptual discrepancies. The most solid solution to overcome this problem is to perform an ontology alignment process capable of leading two heterogeneous ontologies into a mutual agreement by detecting a set of correspondences between them. All ontology alignment processes based on evolutionary approaches developed so far perform an evaluation of the produced alignments based on multi-objectives "a priori" approaches. This paper proposes to apply NSGA II to ontology alignment problem in order to overcome the well-known drawbacks of "a priori" methods. As shown in the experimental section, the application of NSGA II allows to improving semantic interoperability by finding high quality solutions that are not detected by "a priori" approaches.

We consider conditional density approximation by fuzzy systems. Fuzzy systems are typically used to approximate deterministic functions in which the stochastic uncertainty is ignored. We propose probabilistic fuzzy systems (PFSs), in which the probabilistic nature of uncertainty is taken into account. These systems take also fuzzy uncertainty into account by their fuzzy partitioning of input and output spaces. We discuss an additive reasoning scheme for PFSs that leads to the estimation of conditional probability densities and prove how such fuzzy systems compute the expected value of this conditional density function. We show that some of the most commonly used fuzzy systems can compute the same expected output value, and we derive how their parameters should be selected in order to achieve this goal. The additional information and process understanding provided by the different interpretations of the PFS models are illustrated using a real-world example

Value at Risk (VaR) has been successfully estimated using single covariate probabilistic fuzzy systems (PFS), a method which combines a linguistic description of the system behaviour with statistical properties of data. In this paper, we consider VaR estimation based on a PFS model for density forecast of a continuous response variable conditional on a high-dimensional set of covariates. The PFS model parameters are estimated by a novel two-step process. The performance of the proposed model is compared to the performance of a GARCH model for VaR estimation of the S&P 500 index. Furthermore, the additional information and process understanding provided by the different interpretations of the PFS models are illustrated. Our findings show that the validity of GARCH models are sometimes rejected, while those of PFS models of VaR are never rejected. Additionally, the PFS model captures both instant and periods of high volatility, and leads to less conservative models.

Value-at-Risk (VaR) is an important tool to assess portfolio risk. When calculating VaR based on historical stock return data, we hypothesize that this historical data is sensitive to outliers caused by news events in the sampled period. In this paper, we research whether the VaR accuracy can be improved by considering news events as additional input in the calculation. This involves processing the historical data in order to reflect the impact of news on the stock returns. Our experiments show that when an event occurs, removing the noise (that is caused by an event) from the measured stock prices for a small time window can improve VaR predictions.

In the present work, we propose the application of probabilistic fuzzy systems (PFS) to model the prediction of mortality in septic shock patients. This technique is characterized by the combination of the linguistic description of the system with the statistical properties of data. Preliminary results for this particular clinical problem point that PFS models, besides performing as accurately as first order Takagi-Sugeno fuzzy models, also provide probability measures that provide additional clinical information upon which physicians can act on.

Patient Health Records (PHRs) shift the ownership of health data from health providers to patients. Such a shift poses important challenges from the data privacy point of view. Patients would like to be able to selectively reveal information to other stakeholders and, at the same time, be assured that their health information will not be used improperly once shared. Current PHR systems partially fail to satisfy these requirements. In this paper, we show that both requirements can be satisfied fully when adopting a novel cloud-based PHR system architecture.We expain the role of remote virtual machines in this architecture and use interaction models to reason about privacy implications. Finally, we evaluate MyPHRMachines, a prototypical implementation of the architecture: we demonstrate that the system enables the execution of third party genome analysis services on patientowned genome data while ensuring that (1) such services cannot maliciously store this data and (2) patients can show the analysis results to experts without sharing along their full genome.

With the increasing demand for health care, hospitals are looking for ways to optimize their care processes in order to increase efficiency, while guaranteeing the quality of the care. Process modeling is a crucial step for process improvement, since it provides a process model that can be analyzed and optimized. Process mining is a recent promising methodology to discover process models based on data from event logs. However, early applications of process mining to health care has produced overly complex models, which have been attributed to the complexity of the health care domain. In this paper, we argue that existing process mining methods fail to identify good process models, even for well-defined clinical processes. We identify a number of reasons for this shortcoming and discuss a few directions for extending process mining methods in order to make them more suitable for the clinical domain.

Abstract Through its interoperability and reasoning capabilities, the Semantic Web opens a realm of possibilities for developing intelligent systems on the Web. The Web Ontology Language (OWL) is the most expressive standard language for modeling ontologies, the cornerstone of the Semantic Web. However, up until now, no standard way of expressing time and time-dependent information in OWL has been provided. In this paper, we present a temporal extension of the very expressive fragment ${cal SHIN}({cal D})$ of the OWL Description Logic language, resulting in the temporal OWL language. Through a layered approach, we introduce three extensions: 1) concrete domains, which allow the representation of restrictions using concrete domain binary predicates; 2) temporal representation, which introduces time points, relations between time points, intervals, and Allen's 13 interval relations into the language; and 3) timeslices/fluents, which implement a perdurantist view on individuals and allow for the representation of complex temporal aspects, such as process state transitions. We illustrate the expressiveness of the newly introduced language by using an example from the financial domain.

In this paper we build on previous work related to predicting the MSCI EURO index based on content analysis of ECB statements. Our focus is on reducing the number of features employed for prediction through feature selection. For this purpose we rely on two methodologies: (stepwise) linear regression and greedy forward feature subset selection. The original dataset consists of 13 features (General Inquirer content categories). Both methodologies provide an improvement in the overall accuracy of the model, while reducing the number of features employed. Through linear regression we achieve an accuracy of 67.58% on the testing set by relying on six features, while greedy forward selection enables an accuracy on the test set of 69.50% while relying on eight features.

Sentiment analysis has applications in many areas and the exploration of its potential has only just begun. We propose Pathos, a framework which performs document sentiment analysis (partly) based on a document's discourse structure. We hypothesize that by splitting a text into important and less important text spans, and by subsequently making use of this information by weighting the sentiment conveyed by distinct text spans in accordance with their importance, we can improve the performance of a sentiment classifier. A document's discourse structure is obtained by applying Rhetorical Structure Theory on sentence level. When controlling for each considered method's structural bias towards positive classifications, weights optimized by a genetic algorithm yield an improvement in sentiment classification accuracy and macro-level F1 score on documents of 4.5% and 4.7%, respectively, in comparison to a baseline not taking into account discourse structure.

Real-world databases often contain missing data and existing correction algorithms deliver varying performance. Also, most modeling techniques are not suitable to deal with them automatically. In this study we examine different approaches to predicting septic shock in the presence of missing data. Some preprocessing techniques for managing missing data include disregarding data, or replacing it with information that by design introduces bias. In this study, we show that predictive performance improves by employing a minimum pre-processing technique, the Zero-Order-Hold (ZOH) method, by applying a Fuzzy C-Means clustering technique based on the partial distance calculation strategy (FCM-PDS) and by computing the final classification regarding the samples from each patient. Performance improvements continue to occur where up to approximately 60% of the data is missing, though for higher percentage the classification performance still is statistically improved. We further validate this approach by making comparisons with previous studies.

A key element for decision makers to track is their stakeholders' sentiment. Recent developments show a tendency of including various aspects other than word frequencies in automated sentiment analysis approaches. One of these aspects is negation, which can be accounted for in various ways. We compare several approaches to accounting for negation in sentiment analysis, differing in their methods of determining the scope of influence of a negation keyword. On a set of English movie review sentences, the best approach is to consider two words, following a negation keyword, to be negated by that keyword. This method yields a significant increase in overall sentiment classification accuracy and macro-level F1of 5.5% and 6.2%, respectively, compared to not accounting for negation. Additionally optimizing sentiment modification of negated words to a value of -1.27 rather than -1 yields a significant 7.1% increase in accuracy and a significant 8.0% increase in macro-level F1.

To reveal farmers’ motives for on-farm diversification and integration of farming components in the Mekong Delta, Vietnam, we developed a fuzzy logic model (FLM) using a 10-step approach. Farmers’ decision-making was mimicked in a three-layer hierarchical architecture of fuzzy inference systems, using data of 72 farms. The model includes three variables for family motives of diversification, six variables related to component integration, next to variables for the production factors and for farmers’ appreciation of market prices and know-how on 10 components. To obtain a good classification rate of the less frequent activities, additional individual fine-tuning was necessary after general model calibration. To obtain the desired degree of sensitivity to each variable, it was necessary to use up to five linguistic values for some of the input and output variables in the intermediate hierarchical layers. Model’s sensitivity to motivational variables determining diversification and integration was of the same magnitude as its sensitivity to market prices and farmers’ know-how of the activities, but less than its sensitivity to labour, capital and land endowment. Modelling to support strategic decision-making seems too elaborate for individual farms, but FLM will be useful to integrate farmers’ opinions in strategic decision-making at higher hierarchical levels.

Reinforcement learning (RL) is a valuable learning method when the systems require a selection of control actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems and RL, the environment is considered to be deterministic. In many problems, however, the consequence of an action may be uncertain or stochastic in nature. In this paper, we propose a novel RL approach to combine the universal-function-approximation capability of fuzzy systems with consideration of probability distributions over possible consequences of an action. The proposed generalized probabilistic fuzzy RL (GPFRL) method is a modified version of the actor-critic (AC) learning architecture. The learning is enhanced by the introduction of a probability measure into the learning structure, where an incremental gradient-descent weight-updating algorithm provides convergence. Our results show that the proposed approach is robust under probabilistic uncertainty while also having an enhanced learning speed and good overall performance.

Real word data sets often contain many missing elements. Most algorithms that automatically develop a rule-based model are not well suited to deal with incomplete data. The usual technique is to disregard the missing values or substitute them by a best guess estimate, which can bias the results. In this paper we propose a new method for estimating the parameters of a Takagi-Sugeno fuzzy model in the presence of incomplete data. We also propose an inference mechanism that can deal with the incomplete data. The presented method has the added advantage that it does not require imputation or iterative guess-estimate of the missing values. This methodology is applied to fuzzy modeling of a classification and regression problem. The performance of the obtained models are comparable with the results obtained when using a complete data set.

In this paper we use an agent-based stock market to study how investor performance and market predictions influence investor sentiment and confidence. Investor sentiment is modeled using a generalized average operator, which has been proposed in the fuzzy literature as an index of optimism. Our simulations show the impact of loss aversion on investor optimism, and the emergence of investor overconfidence through biased self-attribution. Computational models of financial markets show potential for studying the dynamics of investor psychology with respect to various market feedbacks, while the fuzzy aggregation operator used provides a convenient way of modeling those psychological effects.

In this paper we investigate whether the MSCI EURO index can be predicted based on the content of European Central Bank (ECB) statements. We propose a new model to retrieve information from free text and transform it into a quantitative output. For this purpose, we first identify all adjectives in an ECB statement by using the Stanford Part-of-Speech Tagger and feed these to the General Inquirer (GI) content analysis tool. From GI we obtain a matrix that provides for each document and for each content category the percentage of words in the document that fall under each category. After normalizing the data, we develop a Takagi-Sugeno (TS) fuzzy model using fuzzy c-means clustering. The TS fuzzy system is used to model the levels of the MSCI EURO index. To determine the performance of the model, we focus on the accuracy of predicting upward or downward movement in the index, and obtain, on average, an accuracy of 66%, that corresponds to an improvement of 16% over a random classifier.

Measuring the performance of a given classifier is not a straightforward or easy task. Depending on the application, the overall classification rate may not be sufficient if one, or more, of the classes fail in prediction. This problem is also reflected in the feature selection process, especially when a wrapper method is used. Cohen's kappa coefficient is a statistical measure of inter-rater agreement for qualitative items. It is generally thought to be a more robust measure than simple percent agreement calculation, since it takes into account the agreement occurring by chance. Considering that kappa is a more conservative measure, then its use in wrapper feature selection is suitable to test the performance of the models. This paper proposes the use of the kappa measure as an evaluation measure in a feature selection wrapper approach. In the proposed approach, fuzzy models are used to test the feature subsets and fuzzy criteria are used to formulate the feature selection problem. Results show that using the kappa measure leads to more accurate classifiers, and therefore it leads to feature subset solutions with more relevant features.

Information systems are crucial for effective supply chain management in today's complex supply chains for durable goods. Complex decision making processes on strategic, tactical, and operational level require substantial support in order to contribute to the agility of organizations. Supply chain regimes, i.e., regimes encompassing both the sales and the procurement market in a complex supply chain, provide a way of intuitively and meaningfully characterizing and modeling supply chain market conditions without a need for explicit modeling of individual aspects of the market. This paper makes a first explorative step towards a model incorporating such regimes, while maintaining the dynamics which enable the model to be utilized in the sales process, e.g., for dynamic product pricing. Initial results show that supply chain regimes have feasible characteristics, based on both sales and procurement market indicators. Taking into consideration these regimes enables a more deliberate sales model.

We investigate the effects of adding procurement information (component offer prices) to a sales-based economic regime model, which is used for strategic, tactical, and operational decision making in dynamic supply chains. The performance of the regime model is evaluated through experiments with the MinneTAC trading agent, which competes in the TAC SCM game. We find that the new regime model has a similar overall predictive performance as the existing model. Regime switches are predicted more accurately, whereas the prediction accuracy of dominant regimes is slightly worse. However, by adding procurement information, we have enriched the model and we have further opportunities for applications in the procurement market, such as procurement reserve pricing.

In today's complex supply chains, product pricing is a vital, yet non-trivial task. We propose a product pricing approach using adaptive real-time probability of acceptance estimations based on economic regimes. Radial Basis Function Networks are trained to estimate parameters for double-bounded log-logistic distributions assumed to be underlying daily offer prices, using information available real-time. The relation between data and parameters is dynamically modeled using economic regimes (characterizing market conditions) and error terms (accounting for customer feedback). Given the parametric approximations of price distributions, acceptance probabilities are estimated using a closed-form mathematical expression, which is used to determine the price yielding a desired quota. The approach is implemented in the MinneTAC agent and tested against a price-following product pricing method in the TAC SCM game. Performance significantly improves; more customer orders are obtained against higher prices and profits more than double.

Agent-based artificial financial markets are bottom-up models of financial markets which explore the mapping from the micro level of individual investor behavior into the macro level of aggregate market phenomena. It has been recently recognized in the literature that such (agentbased) models are potentially a very suitable tool to generate or test various behavioral hypotheses. One of the psychological biases that received a lot of attention in financial studies, both mainstream and behavioral, is the phenomena of investor overconfidence. This paper studies overconfident investors in the agent-based artificial financial market based on the Levy, Levy, Solomon (2000) model. Overconfidence is modeled as miscalibration, i.e. as underestimated risk of expected returns. We find that overconfident investors create less frequent but more extreme bubbles and crashes when compared to the unbiased efficient market believers of the original model. When investors are modeled to exhibit a biased self-attribution, they quickly move to the state of high overconfidence and remain there. With an unbiased self-attribution, on the other hand, investor overconfidence varies greatly, but around a moderate level of overconfidence.

This paper proposes extracting fuzzy rules from data using fuzzy possibilistic c-means and possibilistic fuzzy c-means algorithms, which provide more than one partition information: the typicality matrix and the membership matrix. Usually to extract fuzzy rules from data only one of the partition matrix is used, resulting in one rule per cluster. In our work we extract rules from both the membership partition matrix and the typicality matrix, resulting in deriving multiple rules for each cluster. These methods are applied to fuzzy modeling of four different classification problems: Iris, Wine, Wisconsin breast cancer and Altman data sets. The performance of the obtained models is compared and we consider the added value of the proposed approach in fuzzy modeling.

Value at Risk (VaR) measures the worst expected loss of a portfolio over a given horizon at a given confidence level. It summarises the financial risk a company faces into one single number. Recent methods of VaR estimation use parametric conditional models of portfolio volatility to adapt risk estimation to changing market conditions. However, more flexible methods that adapt to the underlying data distribution would be better suited for VaR estimation. In this paper, we consider VaR estimation by using probabilistic fuzzy systems, a semi-parametric method, which combines a linguistic description of the system behaviour with statistical properties of data. The performance of the proposed model is compared to the performance of a GARCH model for VaR estimation. It is found that statistical back testing always accepts PFS models after tuning, while GARCH models may be rejected.

Value at risk (VaR) is a measure for senior management that summarises the financial risk a company faces into one single number. In this paper, we consider the use of fuzzy histograms for quantifying the value-at-risk of a portfolio. It is shown that the use of fuzzy histograms provides a good method of value-at-risk estimation for a portfolio of stocks. The conditional parameters of the model are obtained through minimisation of a test statistic for a VaR back testing method. Evolutionary optimisation is used for this purpose. It is found that statistical back testing always accepts fuzzy histogram models, while the popular GARCH models may be rejected.

The emergence of Web 2.0 and the semantic Web as established technologies is fostering a whole new breed of Web applications and systems. These are often centered around knowledge engineering and context awareness. However, adequate temporal formalisms underlying context awareness are currently scarce. Our focus in this paper is two-fold. We first introduce a new OWL-based temporal formalism - TOWL - for the representation of time, change, and state transitions. Based hereon we present a financial Web-based application centered around the aggregation of stock recommendations and financial data.

This paper proposes a new method of merging parameterized fuzzy sets based on clustering in the parameters space, taking into account the degree of inclusion of each fuzzy set in the cluster prototypes. The merger method is applied to fuzzy rule base simplification by automatically replacing the fuzzy sets corresponding to a given cluster with that pertaining to cluster prototype. The feasibility and the performance of the proposed method are studied using an application in mobile robot navigation. The results indicate that the proposed merging and rule base simplification approach leads to good navigation performance in the application considered and to fuzzy models that are interpretable by experts. In this paper, we concentrate mainly on fuzzy systems with Gaussian membership functions, but the general approach can also be applied to other parameterized fuzzy sets.

The production and delivery of rapidly perishable goods in distributed supply networks involves a number of tightly coupled decision and optimisation problems regarding the just-in-time production scheduling and the routing of the delivery vehicles in order to satisfy strict customer specified time-windows. Besides dealing with the typical combinatorial complexity related to activity assignment and synchronisation, effective methods must also provide robust schedules, coping with the stochastic perturbations (typically transportation delays) affecting the distribution process. In this paper, we propose a novel bi-objective meta-heuristic approach for robust scheduling. The proposed algorithm returns a set of solutions with different cost and risk tradeoffs, allowing the analyst to adapt the planning depending on the attitude to risk. The effectiveness of the approach is demonstrated by a real-world case concerning the production and distribution of ready-mixed concrete.

The participants in a competitive supply chain take their decisions individually in a distributed environment and independent of one another. At the same time, they must coordinate their actions so that the total profitability of the supply chain is safeguarded. This decision problem is known to be a difficult one and the decisions at different stages of the supply chain may lead to large oscillations if not coordinated properly. In this paper, we consider reinforcement learning agents in a multi-echelon supply chain and study under which conditions they are able to manage the supply chain. Q-learning in the well-known beer game is used as a case. It is found that the reinforcement learning agents can learn better policies than humans, although they do not always converge to the optimal policy.

Most agent‐based simulation models of financial markets are discrete‐time in nature. In this paper, we investigate to what degree such models are extensible to continuous‐time, asynchronous modeling of financial markets. We study the behavior of a learning market maker in a market with information asymmetry, and investigate the difference caused in the market dynamics between the discrete‐time simulation and continuous‐time, asynchronous simulation. We show that the characteristics of the market prices are different in the two cases, and observe that additional information is being revealed in the continuous‐time, asynchronous models, which can be acted upon by the agents in such models. Because most financial markets are continuous and asynchronous in nature, our results indicate that explicit consideration of this fundamental characteristic of financial markets cannot be ignored in their agent‐based modeling.

The timely production and distribution of rapidly perishable goods such as ready-mixed concrete is a complex combinatorial optimization problem in the context of supply chain management. The problem involves several tightly interrelated scheduling and routing problems that have to be solved considering a trade-off of production and delivery costs. This paper applies a novel supply chain management paradigm, the distributed optimization, to a real-world case of a concrete delivery supply chain. The production of concrete in several production centers and the distribution of concrete are modeled as job shop problems, where each problem is solved using Ant Colony Optimization. The management methodology consists of allowing each system to exchange information concerning intermediate optimization results through pheromone matrices. In this way, each system finds its own optimization solution based on the information provided by the other systems. A simulation example shows that the proposed coordination mechanism improves the supply chain performance, when compared to another management approach, where both problems are optimized using hybrid methods combining meta-heuristics with constructive heuristics.

In this paper, a knowledge domain visualization approach is applied to the computational intelligence held. A so-called concept map based on the abstracts of the papers presented at the WCCI 2006 is constructed and analyzed. The concept map provides an overview of the computational intelligence field by visualizing the associations between the field's main concepts. To analyze recent developments in the computational intelligence field, the concept map is compared with a concept map based on abstracts of the WCCI 2002. The analysis presented in the paper provides insight into the structure of the computational intelligence field and into the most significant developments in the field during the last years.

Typically, economists develop models by first selecting a model structure based on theoretical considerations and equilibrium conditions, followed by parameter estimation from available data. As more and more data become available about economic processes, the question arises whether it is possible to obtain models in which "data speak for themselves", where both the model structure and the parameter values are identified directly from the data. In this paper, we discuss how genetic programming might be used for this purpose. We propose a framework to formulate a genetic programming search for suitable economic models. We also study a simple case and discuss future directions of research for developing the genetic programming methodology for economic modelling.

Sustainable development in ecological, economic and social dimensions is a major concern for agricultural development. Recent views on sustainable agriculture development recognize farmers as major actors in shaping the development trajectory. In conventional approaches to multi-actor sustainable development modelling, farmers are regarded as utility maximizers. However, this view is incongruent with the daily practice of human decision making, which does not satisfy the conditions for simple utility maximization. Therefore, there is a need for alternative ways to model human decision behavior. In this paper, we consider a fuzzy approach to modelling farmers' decision process. We study farmers' motivations for adopting integrated farming systems in the Vietnamese Mekong delta and develop a hierarchical fuzzy model for this purpose. We design the model by using knowledge from experts, and assess its accuracy by using data obtained from the farmers

Probabilistic fuzzy systems make it possible to model linguistic uncertainty and probabilistic uncertainty in a single system. This paper is concerned with the estimation of the parameters in probabilistic fuzzy classifiers. The purpose of the paper is to introduce a new method that simultaneously estimates all the parameters in a probabilistic fuzzy classifier. The method uses a maximum likelihood criterion and a gradient-based optimization algorithm. The performance of the method is evaluated on two benchmark data sets. The method is compared with a sequential parameter estimation method used in previous publications. Also, a comparison with an alternative method from the literature is made.

Probabilistic fuzzy classifiers are classifier systems that combine fuzzy set theory with probability theory. These classifiers can deal with two different types of uncertainty simultaneously, namely probabilistic uncertainty and fuzziness. Recently, weighted extension of fuzzy clustering has been proposed to design probabilistic fuzzy classifiers for binary classification problems. This method uses a weighting scheme to modify the distances from which the membership values for the fuzzy clusters are determined. The clustering results are influenced by this weighting scheme. We investigate the influence of different types of weighting schemes on the classification performance. A target selection model that has been investigated in previous literature is used as a benchmark. It is observed empirically that a weighting scheme that depends linearly on the deviations from a priori average class probability gives the best clustering results.

The problem of eliciting membership functions for developing expert-knowledge based fuzzy models to evaluate animal production systems is studied. Three existing elicitation methods are considered and a classification of these elicitation methods is given based on the experts' assessment and the response type during the elicitation process. Transition interval estimation is proposed as a new elicitation method that completes our classification. Properties of the proposed method are discussed and are compared to three other elicitation methods from the literature. Guidelines are given on how to select an elicitation method suitable for a given purpose. An illustrative example is given regarding the welfare of laying hens in an egg production system.

We introduce a type of probabilistic fuzzy system with a generalized Mamdani-type fuzzy rule base, and an additive reasoning scheme where conditional probabilities on fuzzy events are aggregated using an interpolation approach. In this way, probabilistic fuzzy outputs can be calculated for arbitrary crisp input vectors. If desired, the probabilistic fuzzy output can be made crisp using a defuzzification and averaging step. Besides introducing the architecture of the probabilistic fuzzy systems and the corresponding equations for calculating the input-output mapping, we summarize some key results from the probability theory and statistics on fuzzy sets. To show the working of the probabilistic fuzzy models introduced, we analyze a simulated GARCH time series using a data-driven approach. A probabilistic fuzzy rule-base is derived from the given data set containing rules that yield a rather good intuitive description of the underlying GARCH-process. Further, we show some additional results like the estimated regression plane and several (un)conditional probability distributions.

Model predictive control (MPC) is a well-known control technique, which has been applied to complex and nonlinear processes. This paper integrates different fuzzy issues in multivariable predictive control. Fuzzy predictive control incorporates fuzzy goals and constraints in model predictive control, in a fuzzy decision making framework. Several issues are proposed in this paper for multivariable fuzzy predictive control, namely, the use of weighted fuzzy decision functions and fuzzy predictive filters. Simultaneous weighted satisfaction of various criteria is modeled by using the qualitative extensions of (Archimedean) fuzzy t-norms. The use of fuzzy predictive filters are represented as an adaptive set of control actions multiplied by gain factors. The integration of the several fuzzy issues proposed in this paper is applied to the control of a container gantry crane. Simulation results show the advantages of the proposed methods.

This paper presents the results of one application of target selection in direct marketing: the mailing campaigns of a charity organization, where the clients are selected based on the expected amount of donation they are going to make. Target selection is an important data mining problem for which several modeling techniques have been used. Statistical regression, neural networks, decision trees, and clustering are the most utilized techniques. Fuzzy clustering can also be applied to target selection. In this paper, traditional and fuzzy techniques are compared by using cross-validation measures. The four techniques are applied based on recency, frequency and monetary value measures. The application to mailing campaigns of a charity organization, showed that fuzzy modeling obtains results similar to those of other classical target selection techniques.

This article presents two techniques to improve the calculation of the fuzzy covariance matrix in the Gustafson-Kessel (GK) clustering algorithm. The first one overcomes problems that occur in the standard GK clustering when the number of data samples is small or when the data within a cluster are linearly correlated. The improvement is achieved by fixing the ratio between the maximal and minimal eigenvalue of the covariance matrix. The second technique is useful when the GK algorithm is employed in the extraction of Takagi-Sugeno fuzzy model from data. It reduces the risk of overfitting when the number of training samples is low in comparison to the number of clusters. This is achieved by adding a scaled unity matrix to the calculated covariance matrix. Numerical examples are presented to demonstrate the benefits of the proposed techniques

Design of fuzzy classifiers based on probabilistic fuzzy systems is considered. It is shown that the statistical properties of the training data can be used for the design of fuzzy rule based classification systems. Takagi-Sugeno type fuzzy systems are designed for estimating the underlying conditional probability density function for the data. Probabilistic rule weighting is introduced, and classifiers based on the discriminant function approach are formulated. It is shown that some of the fuzzy classifiers that have been proposed in the literature can be formulated in terms of probabilistic rule weighting. Furthermore, the relation to certainty factor approach to fuzzy classifiers is considered.

Target selection in direct marketing is an important data mining problem for which fuzzy modeling can be used. The paper compares several fuzzy modeling techniques applied to target selection based on recency, frequency and monetary value measures. The comparison uses cross validation applied to mailing campaigns of a charity organization.

Two extensions to objective function-based fuzzy clustering are proposed. First, the (point) prototypes are extended to hypervolumes, whose size can be fixed or can be determined automatically from the data being clustered. It is shown that clustering with hypervolume prototypes can be formulated as the minimization of an objective function. Second, a heuristic cluster merging step is introduced where the similarity among the clusters is assessed during optimization. Starting with an overestimation of the number of clusters in the data, similar clusters are merged in order to obtain a suitable partitioning. An adaptive threshold for merging is proposed. The extensions proposed are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resulting extended algorithm is given. The properties of the new algorithm are illustrated by various examples.

After recapitulating various basic notions from classical probability theory and statistics, this theory is generalized to a probabilistic and statistical framework defined on fuzzy sets. Using the new framework, the competitive exception learning algorithm is presented, described and discussed.

Many practical optimization problems are characterized by some flexibility in the problem constraints, where this flexibility can be exploited for additional trade-off between improving the objective function and satisfying the constraints. Fuzzy sets have proven to be a suitable representation for modeling this type of soft constraints. The paper proposes an extension of this model for satisfying the problem constraints and the goals, where preference for different constraints and goals can be specified by the decision-maker. The difference in the preference for the constraints is represented by a set of associated weight factors, which influence the amount of trade-off between improving the optimization objectives and satisfying various constraints. Simultaneous weighted satisfaction of the problem constraints and goals are demonstrated by using a fuzzy linear programming problem.

Advances in computational methods have led, in the world of financial services, to huge databases of client and market information. In the past decade, various computational intelligence techniques have been applied in mining this data for obtaining knowledge and in-depth information about the clients and the markets. The paper discusses the application of fuzzy clustering in target selection from large databases for direct marketing purposes. Actual data from the campaigns of a large financial services provider are used as a test case. The results obtained with the fuzzy clustering approach are compared with those resulting from the current practice of using statistical tools for target selection.

Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the control problem is combined by using a decision function from the theory of fuzzy sets. This paper investigates the use of fuzzy decision making (FDM) in model predictive control (MPG), and compares the results to those obtained from conventional MPG. Attention is also paid to the choice of aggregation operators for fuzzy decision making in control. Experiments on a nonminimum phase, unstable linear system, and on an air-conditioning system with nonlinear dynamics are studied. It is shown that the performance of the model predictive controller can be improved by the use of fuzzy criteria in a fuzzy decision making framework.

Discusses some essential requirements for the introduction of computational intelligence techniques in the field of financial services, and reports on an investigation carried out concerning the possibilities and expected success of using fuzzy systems in some business chapters of the Dutch ING group. Based on this investigation, the subject of direct marketing is chosen for a pilot study of the application of data-driven modeling techniques using fuzzy clustering and iterative gain-charts refinement. This is compared with the present practice using statistical tools.

In fuzzy rule-based models acquired from numerical data, redundancy may be present in the form of similar fuzzy sets that represent compatible concepts. This results in an unnecessarily complex and less transparent linguistic description of the system. By using a measure of similarity, a rule base simplification method is proposed that reduces the number of fuzzy sets in the model. Similar fuzzy sets are merged to create a common fuzzy set to replace them in the rule base. If the redundancy in the model is high, merging similar fuzzy sets might result in equal rules that also can be merged, thereby reducing the number of rules as well. The simplified rule base is computationally more efficient and linguistically more tractable. The approach has been successfully applied to fuzzy models of real world systems.