Author(s)

R. P. Espíndola & N. F. F. Ebecken

Abstract

This paper introduces a fuzzy decision tree to initiate the first population of a genetic algorithm to perform data classification. On large datasets, the evolutive process tends to waste computational resources until some good individual is found. It is expected that the use of a fuzzy decision tree can significantly reduce this feature. The genetic algorithm aims to obtain small fuzzy classifiers by means of optimization of fuzzy rules bases. 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 number of features employed. The results obtained are compared with 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. Keywords: classification, feature selection, fuzzy systems, genetic algorithms, fuzzy decision tree. 1 Introduction One of the major drawbacks of a genetic algorithm is the high computational costs on performing its search. When dealing with large datasets, this feature is a key aspect to be considered. In this work, feature selection [1] and classification [2] are performed by a fuzzy genetic system. Fuzzy rules are generated automatically from the datasets and a genetic algorithm is applied to find the shortest and most accurate subset of rules. As each rule employs only one feature, the final subset possibly uses few features. In this model of rules, the classification is done along with a value which estimates the relationship between the condition and the class, defined by the concept of fuzzy subsethood.

Keywords

classification, feature selection, fuzzy systems, genetic algorithms, fuzzy decision tree.