A fuzzy decision tree approach to start a genetic algorithm for data classification

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.