Data classification by a fuzzy genetic system approach
Rogério Pinto Espíndola, Nelson F. F. Ebecken · WIT transactions on information and communication technologies · 2003
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.