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Evolutionary Adaptive Defuzzification Methods are a kind of defuzzification methods based on using a parametrical defuzzification expression tuned with evolutionary algorithms. Their goal is to increase the accuracy of the fuzzy system without loosing its interpretability. They induce a kind of rule cooperation in the defuzzification interface. This paper deals with Evolutionary Adaptive Defuzzification Methods. We study their common general expression, the different defuzzification methods that can be obtained from it, their interpretation, and their accuracy. We consider two applications in order to analyze their accuracy in practice. We get some useful results for practical fuzzy systems designed by means of this kind of Intelligent Hybrid System. [ABSTRACT FROM AUTHOR]

Irony detection is a not trivial problem and can help to improve natural language processing tasks as sentiment analysis. When dealing with social media data in real scenarios, an important issue to address is data skew, i.e. the imbalance between available ironic and non-ironic samples available. In this work, the main objective is to address irony detection in Twitter considering various degrees of imbalanced distribution between classes. We rely on the emotIDM irony detection model. We evaluated it against both benchmark corpora and skewed Twitter datasets collected to simulate a realistic distribution of ironic tweets. We carry out a set of classification experiments aimed to determine the impact of class imbalance on detecting irony, and we evaluate the performance of irony detection when different scenarios are considered. We experiment with a set of classifiers applying class imbalance techniques to compensate class distribution. Our results indicate that by using such techniques, it is possible to improve the performance of irony detection in imbalanced class scenarios. [ABSTRACT FROM AUTHOR]

The article discusses various papers published within the issue, including one on support vector machines, one on the analysis of the evolutionary learning algorithm XCS and one two more on case-based reasoning.

DataMining is most commonly used in attempts to induce association rules from transaction data which can help decision-makers easily analyze the data and make good decisions regarding the domains concerned. Most conventional studies are focused on binary or discrete-valued transaction data, however the data in real-world applications usually consists of quantitative values. In the last years, many researches have proposed Genetic Algorithms for mining interesting association rules from quantitative data. In this paper, we present a study of three genetic association rules extraction methods to show their effectiveness for mining quantitative association rules. Experimental results over two real-world databases are showed. [ABSTRACT FROM AUTHOR]

This paper presents an evolutionary learning process for linguistic modeling with weighted double-consequent fuzzy rules. These kinds of fuzzy rules are used to improve the linguistic modeling, with the aim of introducing a trade-off between interpretability and precision. The use of weighted double-consequent fuzzy rules makes more complex the modeling and learning process, increasing the solution search space. Therefore, the cooperative coevolution, an advanced evolutionary technique proposed to solve decomposable complex problems, is considered to learn these kinds of rules. The proposal has been tested with different problems achieving good results. [ABSTRACT FROM AUTHOR]

The use of Mamdani-type fuzzy rule-based systems (FRBSs) allows us to deal with the modeling of systems building a linguistic model clearly interpretable by human beings. However, the accuracy obtained is not sometimes as good as desired. This fact relates to the restriction imposed when using linguistic variables, which forces the membership functions considered in each fuzzy linguistic rule to belong to a common set of them, i.e., to use a global grid. To solve this problem, in the last few years a new variant has been proposed working directly with fuzzy variables in the fuzzy rules instead of linguistic terms, thus ignoring the said restriction. Therefore, these systems, which are totally equivalent to fuzzy graphs (defined by Zadeh as granular representations of functional dependencies and relations), do not consider a global grid and could be named non-grid-oriented (NGO) FRBSs. Of course, the main objective of these models is the accuracy of the system instead its interpretability. Until now, NGO FRBSs have been little considered and developed in the literature. However, and due to their good accuracy, their use is increasing thus making necessary a wide analysis on the features and associated learning methods in the NGO domain. This contribution aims at analyzing the structure and framework of NGO FRBSs, as well as making a taxonomy of learning methods considering the constrains imposed on the fuzzy sets in the generation process. Some automatic learning techniques and methods proposed in the literature to build these fuzzy graphs will be also reviewed and analyzed when solving several applications of different nature. [ABSTRACT FROM AUTHOR]