Author(s)

R. P. Espíndola & N. F. F. Ebecken

Abstract

Data classification by a fuzzy genetic system approach R. P. Espindola & N. F. F. Ebecken COPPE/Federal University of Rio de Janeiro, Brazil Abstract This paper presents a fuzzy genetic approach to perform data classification. The method aims to obtain small fuzzy classifiers by means of optimization of fuzzy rules bases using a genetic algorithm. It is shown how a fuzzy rules base is generated from a numerical database and how its best subset is found by the genetic algorithm. The classifiers are evaluated in terms of accuracy, cardinality and amounts of features employed. The results obtained are compared to a known study in the literature and with an academic decision tree tool. The method was able to produce small fuzzy classifiers with very good performance. 1 Introduction Classification (Gordon, [I]) is an important task in each knowledge field. It consists of classifying elements described by a fixed set of attributes into one of a finite set of categories or classes. For example, to di

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