This paper presents FISDeT, a Python tool that enables the design of a Fuzzy Inference System (FIS) based on the standard language FCL. FISDeT includes a GUI that enables the user to easily define and update the rule base of a FIS. Given a FIS, the tool can perform the inference of fuzzy rules. To show the main features of FISDeT, in this paper we employ the tool to develop fuzzy rule-based systems that can solve the problem of beer style classification. The integrated testing facilities of FISDeT enable a comparison among the created classifiers.

We propose an approach to integrate the KEEL software tool for knowledge discovery within the KNIME Analytics platform. The integration approach is non-invasive as it does not require the modification of source code in neither of the tools. As a result of the integration, it is possible to use the algorithms provided with KEEL - including many fuzzy methods - directly in KNIME workflows, thus taking the advantages of both tools. We report two simple integration examples, which show the effectiveness of the proposed approach in building data analysis workflows involving KEEL methods, possibly along with methods provided by other knowledge discovery tools like WEKA.

In this paper, we present an incremental clustering approach for shape annotation, which is useful when new sets of images are available over time. A semi-supervised fuzzy clustering algorithm is used to group shapes into a number of clusters. Each cluster is represented by a prototype that is manually labeled and used to annotate shapes belonging to that cluster. To capture the evolution of the image set over time, the previously discovered prototypes are added as pre-labeled objects to the current shape set and semi-supervised clustering is applied again. The proposed incremental approach is evaluated on two benchmark image datasets, which are divided into chunks of data to simulate the progressive availability of images during time.

When approaching real-world problems with intelligent systems, an interaction with user is often expected. However, data-driven models are usually evaluated only in terms of accuracy, thus not involving users. In literature several works have been proposed for defining measures for interpretability assessment, however, such measures are mostly based on a structural evaluation. For this reason, we investigated a new methodology for assessing interpretability based on semantic cointension. The objective of this work is to provide empirical evidence about the usefulness of semantic cointension in facing a medical problem, namely the prediction of prognosis in Immunoglobulin A Nephropathy. An experimental session has been conducted, where fuzzy rule-based classifiers have been modeled, which are highly interpretable from the structural viewpoint. Results show that through the notion of semantic cointension it is possible to perform a semantic-driven assessment of interpretability, which also takes into account the overall fuzzy inference schema.

Situation-aware service recommendation for mobile devices is aimed at proactively pushing personalized suggestions to users, presenting them unseen or unknown services. A challenging area in the field is that of recommendation schemes emerging from users' collective behavior. When we consider a mobile user, for instance, the recommendation process can be based on social events that can arise from collective positioning information. In this scenario, we discuss a collaborative multi-agent scheme for event detection, in which fuzzy representations are employed to cope with the approximation typical of implicit and aggregated information. More specifically, the first level of information processing is managed by marking agents leaving marks in the environment which are associated with users' positioning. The accumulation of marks enables a fuzzy information granulation process, managed by event agents, in which relevant events can emerge. Finally, a fuzzy inference level, managed by situation agents, deduces user situations from the underlying events.

A key feature for machine intelligence is the ability of learning knowledge from past experiences. Furthermore, in a human-centric environment, the acquired knowledge must fulfill comprehensibility requirements so as to be shared by human users. In literature, several approaches have been proposed to acquire comprehensible knowledge from data by preserving a number of interpretability constraints, especially for Fuzzy Rule-Based Classifiers (FRBCs). As a general result, accuracy and interpretability emerge as conflicting features, so that a tradeoff is often required. In consequence of this tradeoff, the resulting FRBCs are provided with a knowledge base expressed in natural language but, as a matter of fact, the semantics embedded by the linguistic structures might not be cointensive with the explicit semantics defined in the knowledge base. As an alternative approach, in this paper we propose a technique to design FRBCs from data with the specific aim of maximizing interpretability in the sense of semantic cointension. The most important result of this approach is to control cointension so as to select models that possess knowledge bases that users can understand on the basis of their natural language description. This enables the use of the FRBC in a human-centric environment. Experimental sessions are performed on benchmark classification problems to show the effectiveness of the proposed approach.

In recommender systems, the task of automatically deriving user profiles, encoding the actual preferences of users, covers a fundamental role. In this paper, we propose a strategy for learning and updating user profiles by using fuzzy sets that reveal to be a valid tool to model the vague and imprecise nature of preferences as well as the items to be recommended. The proposed adaptation strategy resembles a competitive learning process in which the user profile is continuously updated in order to make its components as similar as possible to the description of the accessed items. On the same time a mechanism to forget outdated user preferences is proposed in order to describe changes in user interests over time. The strategy was applied on the MovieLens dataset and the obtained results show its effectiveness to learn user profiles reflecting the current preferences of users.

Recommender systems are systems capable of assisting users by quickly providing them with relevant resources according to their interests or preferences. The efficacy of a recommender system is strictly connected with the possibility of creating meaningful user profiles, including information about user preferences, interests, goals, usage data and interactive behavior. In particular, analysis of user preferences is important to predict user behaviors and make appropriate recommendations. In this paper, we present a fuzzy framework to represent, learn and update user profiles. The representation of a user profile is based on a structured model of user cognitive states, including a competence profile, a preference profile and an acquaintance profile. The strategy for deriving and updating profiles is to record the sequence of accessed resources by each user, and to update preference profiles accordingly, so as to suggest similar resources at next user accesses. The adaption of the preference profile is performed continuously, but in earlier stages it is more sensitive to updates (plastic phase) while in later stages it is less sensitive (stable phase) to allow resource recommendation. Simulation results are reported to show the effectiveness of the proposed approach.

Interpretability represents the most important driving force behind the implementation of fuzzy logic-based systems. It can be directly related to the system's knowledge base, with reference to the human user's easiness experienced while reading and understanding the embedded pieces of information. In this paper, we present a preliminary study on interpretability conditions for fuzzy rule-based classifiers on the basis of an innovative approach that relies on the concept of semantic cointension. The approach adopted in this study consists in analysing the components of a fuzzy classifiers so that inference is carried out with the respect of logical properties. As a result, we derive some sufficient conditions and basic requirements to be verified by a fuzzy classifier in order to be tagged as interpretable in the semantic sense.

Item recommendation depends on metadata describing items as well as users through their profiles. Most currently used technologies use precise metadata because of the efficiency of the recommendation process. Nonetheless fuzzy metadata can be useful because of their ability to deal with imprecision and gradedness, two features pervading real-world applications. Fuzzy metadata can have both possibilistic and veristic interpretations, which are complementary and can simultaneously occur in a recommendation context. In this paper we describe a preliminary approach to deal with this double interpretation proposing an extension of the theory of veristic variables, that is specifically suited for item recommendation. Fuzzy metadata are used to calculate the interestingness of an item for a user computing possibility and necessity measures, which enable the ranking of items. As described in the illustrative examples, this approach effectively provides for semantically significant results that are useful for item recommendation with fuzzy metadata.

Recommender systems attempt to predict the needs of Web users and provide them with recommendations to personalize their online experience. In this paper, we propose a neuro-fuzzy approach for the extraction of a recommendation model from usage data encoding user navigational behaviors. Such model is expressed as a set of fuzzy rules which may be exploited to provide personalized link suggestions to the users visiting a Web site. In particular, a neuro-fuzzy network is trained using information about user categories to discover a set of fuzzy rules capturing the associations between user behavior models and relevance degrees of pages to be recommended. A comparison with other recommendation approaches shows the effectiveness of the proposed neuro-fuzzy approach in finding good recommendation rules.

Computational Intelligence (CI) paradigms reveal to be potential tools to face under the Web uncertainty. In particular, CI techniques may be properly exploited to handle Web usage data and develop Web-based applications tailored on users preferences

In this paper we present an approach for extracting interpretable information granules for classification. The approach, called DCγ(double clustering with genetic algorithms) is based on two clustering steps. The first step uses LVQ1 to identify cluster prototypes in the multidimensional data space so as to represent hidden relationships among data. In the second step a genetic algorithm is applied to the projections of these prototypes with the objective of finding a minimal number of fuzzy information granules that verify some interpretability constraints. The key feature of DCγis the efficiency of the minimization process carried out in the second step. Experimental results on two medical diagnosis problems show the effectiveness of the proposed approach in terms of accuracy, interpretability and efficiency.

In this paper we describe an approach for mining interpretable diagnostic rules through a fuzzy information granulation process. Specifically, this process is performed by the DC* algorithm (Double Clustering with A*), which is aimed at mining from data a set of fuzzy information granules that satisfy a number of interpretability constraints. Such granules can be labelled with linguistic terms and used as building blocks for deriving diagnostic rules. The DC* is based on two clustering steps. The first step applies the LVQ1 algorithm to find a number of prototypes in the input space, which represent hidden relationships among data. The second clustering step .based on the A* search. takes place on the projections of such prototypes, and is aimed at finding an optimal number of granules that verify interpretability constraints. The application of DC* to two well-known medical datasets provided a set of intelligible rules with satisfactory accuracy.

Data Mining, a central step in the broader overall process of Knowledge Discovery from Databases, concerns with discovering useful properties, called patterns, from data. Understandability is an essential — yet rarely tackled — feature that makes resulting patterns accessible by end users. In this paper we argue that the adoption of Fuzzy Logic for Data Mining can improve understandability of derived patterns. Indeed, Fuzzy Logic is able to represent concepts in a “human-centric” way. Hence, Data Mining methods based on Fuzzy Logic may potentially meet the so-called “Comprehensibility Postulate”, which characterizes the blurry notion of understandability. However, the mere adoption of Fuzzy Logic for Data Mining is not enough to achieve understandability. This paper describes and comments a number of issues that need to be addressed to provide for understandable patterns. A careful consideration of all such issues may end up in a systematic methodology to discover comprehensible knowledge from data.

In this paper we present a multi-level approach for extracting well-defined and semantically sound information granules from numerical data. The approach is based on the Double Clustering framework (DC/), which performs two main clustering steps on the data space in order to extract granules qualitatively described in terms of fuzzy sets that meet a number of interpretability constraints. While DC/ can extract information granules with a fixed level of granulation, its multi-level extension, called ML-DC (Multi-Level Double Clustering), can perform granulation of data at different levels, in a hierarchical fashion. At the first level, the whole dataset is granulated. At the second level, data embraced in each first-level granule are further granulated taking into account the context generated by that granule. The hierarchical collection of granules derived via ML-DC is then used to construct a committee of fuzzy inference systems that can approximate any I/O mapping with a good balance between accuracy and interpretability.

In this paper we propose a meta-classification framework which is able to represent the accumulated experience from a base-learner in form of knowledge-base and to exploit it whenever a new task has to be tackled. Our meta-dassificr represents a particular meta-learning strategy where a single learning algorithm is employed both at base- and meta-level of learning. The proposed meta-classification framework is based on a neuro-fuzzy approach where connectionist paradigm is integrated with fuzzy logic for the management of the accumulated knowledge during the learning process. A complete experimental session is presented, in order to show the suitability of the system. Additionally, in a final experimental evaluation, we show that the obtained accuracy results are comparable with the ones obtained by another meta classification scheme presented in literature.

This paper describes a peculiar methodology for identifying and classifying the human body structures depicted in digital images. The methodology is articulated in a number of successive steps: an initial process of motion detection recognises human targets into the examined scene; subsequently, a synthetic representation of the previously detected human figures is obtained in form of skeleton, by means of a thinning operator. Finally, the pose estimation step is performed on the basis of a geometrical analysis conducted over a set of features related to the extracted skeletons. Experimental evidence of the appropriateness of the overall methodology is also presented.

The employment of image processing techniques appears to be wide-spreading in several application areas, with special reference to the medical context. A neuro-fuzzy approach for pixel classification is detailed and its application is proposed to the analysis of dermatological images of nevi. An ensemble of experimental results is provided to demonstrate the usefulness of the introduced methodology and to show the possible benefits deriving from its employment in clinical diagnosis.

The paper proposes a new Empirical Risk Functional as cost function for training neuro-fuzzy classifiers. This cost function, called Approximate Differentiable Empirical Risk Functional (ADERF), provides a differentiable approximation of the misclassification rate so that the Empirical Risk Minimization Principle formulated in Vapnik's Statistical Learning Theory can be applied. Also, based on the proposed ADERF, a learning algorithm is formulated. Experimental results on a number of benchmark classification tasks are provided and comparison to alternative approaches given.

In this paper an approach for automatic discovery of transparent diagnostic rules from data is proposed. The approach is based on a fuzzy clustering technique that is defined by three sequential steps. First, our Crisp Double Clustering algorithm is applied on available symptoms measurements, to provide a set of representative multidimensional prototypes that are further clustered onto each one-dimensional projection. The resulting clusters are used in the second step, where a set of fuzzy relations are defined in terms of transparent fuzzy sets. As a final step, the derived fuzzy relations are employed to define a set of fuzzy rules, which establish the knowledge base of a fuzzy inference system that can be used for fuzzy diagnosis. The approach has been applied to the Aachen Aphasia dataset as a real-world benchmark and compared with related work.

A staged approach to identify a compact fuzzy classification rule base from numerical data is presented. First, the fuzzy rules are generated by adaptively clustering the input data and defining a relationship between cluster membership values and class labels. Then, the classification accuracy of the resulting fuzzy rules is enhanced by training a neuro-fuzzy network used to model the fuzzy classifier. Finally, the interpretability of the resulting fuzzy classifier is improved via a compression of the fuzzy rule base. Two well known data classification problems are considered to asses the validity of the approach.

A self-organizing neural network is proposed which is inherently a fuzzy inference system with the capability of learning fuzzy rules from data. The learning strategy consists of two phases: a self-organizing clustering to establish the structure of the network as well as the initial values of its parameters and a supervised learning phase for optimal adjustment of these parameters. After learning, the network encodes in its structure the essential design parameters of a fuzzy system. An example is given to illustrate the characteristics and capabilities of the proposed network.

Feature selection is an integral part of most learning algorithms. By selecting relevant features of the data, higher predictive accuracy or classification rate can be expected from a machine learning method. We propose an approach to feature selection based on neural network pruning. The method performs a backward selection by successively removing input nodes in a network trained with the complete set of features as inputs. When an input node is removed, and relative weight connections are excised, the remaining weights are updated so as to keep approximately unchanged the behavior of the network. A simple criterion to select input nodes to be removed is developed. Experimental results over a well-known classification problem show the feasibility of the proposed approach and encourage its application to other classification tasks.

The problem of determining the proper size of an artificial neural network is recognized to be crucial, especially for its practical implications in such important issues as learning and generalization. One popular approach for tackling this problem is commonly known as pruning and it consists of training a larger than necessary network and then removing unnecessary weights/nodes. In this paper, a new pruning method is developed, based on the idea of iteratively eliminating units and adjusting the remaining weights in such a way that the network performance does not worsen over the entire training set. The pruning problem is formulated in terms of solving a system of linear equations, and a very efficient conjugate gradient algorithm is used for solving it, in the least-squares sense. The algorithm also provides a simple criterion for choosing the units to be removed, which has proved to work well in practice. The results obtained over various test problems demonstrate the effectiveness of the proposed approach.

We address the problem of training relaxation labeling processes, a popular class of parallel iterative procedures widely employed in pattern recognition and computer vision. The approach discussed here is based on a theory of consistency developed by Hummel and Zucker (1983) and contrasts with a previously introduced learning strategy which can be regarded as heteroassociative, i.e. what is actually learned is the association between patterns rather than the patterns themselves. The proposed learning model is instead autoassociative and involves making a set of training patterns consistent in the sense rigorously defined by Hummel and Zucker; this implies that they become local attractors of the relaxation labeling dynamical system. The learning problem is formulated in terms of solving a system of linear inequalities, and a straightforward iterative algorithm is presented to accomplish this. The learning model described here allows one to view the relaxation labeling process as a kind of asymmetric associative memory, the effectiveness of which is demonstrated experimentally.

An adaptive predictor is proposed in which the decision rule takes into account the dynamic behaviour of a set of fixed predictors in an assigned neighborhood of the actual pel. The differences between the proposed rule and the most usual criteria are outlined. The results of the measurements show that the dynamic rule performances are at least on a level with the performances of the other criteria examined, and the point of view of predictor sensitivity to changes in image statistics, the dynamic rule may even be preferable.

Some predictive encoding schemes are applied to the progressive reconstruction of news photos stored in screened form. Experimental results obtained on pictures received on the analog telephoto channel by means of a suitable electronic system are discussed. A switched predictor, which takes into account the RMS prediction error resulting from previous predictions to carry out the choice, is shown to perform significantly better than some classical prediction schemes.