Modern human societies have evolved into an almost entirely connected world, giving place to a remarkable increase in social interactions. In this new context and because of the globalization of all human activities, the collective participation in decision-making processes takes an increasingly prominent role. In this paper, a method for group decision making from a set of imprecise opinions called “moviQuest Decision Making” (MQDM), is presented. This method allows to integrate the opinions of heterogeneous groups of agents in a structured social network along a sequence of voting rounds for collective decision making.

Big data has become one of the emergent topics when learning from data is involved. The notorious increment in the data generation has directed the attention towards the obtaining of effective models that are able to analyze and extract knowledge from these colossal data sources. However, the vast amount of data, the variety of the sources and the need for an immediate intelligent response pose a critical challenge to traditional learning algorithms. To be able to deal with big data, we propose the usage of a linguistic fuzzy rule based classification system, which we have called Chi-FRBCS-BigData. As a fuzzy method, it is able deal with the uncertainty that is inherent to the variety and veracity of big data and because of the usage of linguistic fuzzy rules it is able to provide an interpretable and effective classification model. This method is based on the MapReduce framework, one of the most popular approaches for big data nowadays, and has been developed in two different versions: Chi-FRBCS-BigData-Max and Chi-FRBCS-BigData-Ave. The good performance of the Chi-FRBCS-BigData approach is supported by means of an experimental study over six big data problems. The results show that the proposal is able to provide competitive results, obtaining more precise but slower models in the Chi-FRBCS-BigData-Ave alternative and faster but less accurate classification results for Chi-FRBCS-BigData-Max.

The estimation of the quality of the learned models in Data Mining has been traditionally carried out by means of a k-fold partition technique. However, the “random” division of the instances over the folds may results in a problem known as covariate shift, i.e. there is a different data distribution between the training and test folds. In classification with imbalanced datasets this problem is more severe. The misclassification of minority class instances due to an incorrect learning of the real boundaries caused by a not well defined data distribution, truly affects the measures of performance in this scenario. To avoid this harmful situation, we propose the use of a specific validation technique for the partitioning of the data, known as “Distribution optimally balanced stratified cross-validation”. This methodology makes the decision of placing close-by samples on different folds, so that each partition will end up with enough representatives of every region. In this contribution, we show the goodness of this methodology using Genetic Fuzzy Systems, as they are known to be robust approaches for all types of classification problems. Specifically, we have chosen the FARC-HD algorithm, a novel technique which has shown to obtain very accurate results. From the experimental analysis, which is carried out on a wide number of imbalanced datasets, we emphasize the necessity of using a proper validation methodology for extracting well founded conclusions.

Discretization is an essential preprocessing technique used in many knowledge discovery and data mining tasks. Its main goal is to transform a set of continuous attributes into discrete ones, by associating categorical values to intervals and thus transforming quantitative data into qualitative data. In this manner, symbolic data mining algorithms can be applied over continuous data and the representation of information is simplified, making it more concise and specific. The literature provides numerous proposals of discretization and some attempts to categorize them into a taxonomy can be found. However, in previous papers, there is a lack of consensus in the definition of the properties and no formal categorization has been established yet, which may be confusing for practitioners. Furthermore, only a small set of discretizers have been widely considered, while many other methods have gone unnoticed. With the intention of alleviating these problems, this paper provides a survey of discretization methods proposed in the literature from a theoretical and empirical perspective. From the theoretical perspective, we develop a taxonomy based on the main properties pointed out in previous research, unifying the notation and including all the known methods up to date. Empirically, we conduct an experimental study in supervised classification involving the most representative and newest discretizers, different types of classifiers, and a large number of data sets. The results of their performances measured in terms of accuracy, number of intervals, and inconsistency have been verified by means of nonparametric statistical tests. Additionally, a set of discretizers are highlighted as the best performing ones.

The currently-in-use definition of aggregation function is analyzed in this paper, noting that the introduced variability in the dimension of information does not avoid some obvious dysfunctions. In particular, a potential abuse of the mathematical formalism underlies such a definition, which could lead to solve a complex concept by means of a formal mathematical expression. In this paper we propose an alternative definition making emphasis on the practical implementation of aggregation functions, taking into account the objectives and limitations observed in the application of aggregation functions within the fuzzy context.

Classification in imbalanced domains has become one of the most relevant problems within the area of Machine Learning at the present. This problem has raised in significance due to its presence in many real applications and it occurs when the distribution of the available examples to carry out the learning process is very different between the classes (often for binary class data-sets). Usually, the underrepresented class is the concept of the most interest for the problem, being the cost derived from a misclassification of these examples much higher than that of the remaining examples. In this work we analyze the behaviour of a cost-sensitive learning method for Fuzzy Rule Based Classification Systems in the scenario of high imbalanced data-sets. Specifically, we focus on one representative rule learning approach for Genetic Fuzzy Systems, the Fuzzy Hybrid Genetics-Based Machine Learning algorithm. The experimental results show how our cost-sensitive approach in this type of domains will help us to obtain very accurate solutions in shorter training times and also with a lower complexity with respect to other possibilities proposed for classification with imbalanced problems such as the use of preprocessing to rebalance the class distribution.

Since many complex decision making problems can be solved solely by means of an appropriate algorithm, checking the quality of such algorithm is a key issue, even more relevant in the presence of fuzzy uncertainty. In this paper we postulate that the design and formal specification of algorithms can be translated into a fuzzy framework introducing fuzzy first order logic and assert transformations. Following the classical crisp scheme we first formalize the concepts of a fuzzy algorithm specification and a fuzzy computing state, and then a new fuzzy computational logic is presented, so we can derive a computational reasoning for correctness of algorithms. A proposal for the evaluation and setting of suitable degrees of truth to computing states is also introduced.