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Fuzzy classification systems have been widely researched with many approaches proposed in the literature. Several methods are available for the automatic definition of fuzzy classification systems, which basically comprehend two tasks: i) the definition of the attributes in terms of fuzzy sets, and ii) the generation of a rule set containing the domain knowledge, named fuzzy rule base. Genetic Fuzzy...
This paper presents further experiments for the FUZZY-WRAPPER, a feature subset selection method based on the Wang & Mendel method to generate fuzzy rule bases. This method aims at providing a means of selecting features taking into consideration aspects of fuzzy logic based representations, such as number, shape, and distribution of fuzzy sets, and reasoning method. The main idea is to consider...
The dimension of a knowledge domain can impact the use of genetic algorithms to automatically design fuzzy rule bases, since the search space for the genetic algorithm increases exponentially with the number of features. Filters are a possible approach to reduce the number of features. However, the filter approach does not take into consideration the particular aspects of fuzzy logic when selecting...
The definition of the fuzzy rule base is one of the most important and difficult tasks when designing fuzzy systems. This paper discusses the results of two different hybrid methods investigated earlier, for the automatic generation of fuzzy rules from numerical data. One of the methods proposes the creation of fuzzy rule bases using genetic algorithms in association with a heuristic for preselecting...
The definition of the Rule Base is one of the most important and difficult tasks when designing Fuzzy Systems. A method for the generation of fuzzy rule bases using genetic algorithm, including a phase of preselection of candidate rules, has been proposed by the authors. The selection of candidate rules uses criteria based on heuristics related to the degree of coverage of the rules. This paper proposes...
Traditional algorithms for learning Bayesian classifiers (BCs) from data are known to induce accurate classification models. However, when using these algorithms, two main concerns should be considered: i) they require qualitative data and ii) generally the induced models are not easily comprehensible by human beings. This paper deals with the two above issues by proposing a hybrid method named BayesFuzzy...
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