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Genetic Feature Selection in a Fuzzy Rule-Based Classification System Learning Process for High Dimensional Problems

By J. Casillas, O. Cordón, M. J. Del Jesus, F. Herrera, Jorge Casillas and Francisco Herrera

Abstract

The inductive learning of a Fuzzy Rule-Based Classification System (FRBCS) is made difficult by the presence of a high feature number that increases the dimensionality of the problem being solved. The difficulty comes from the exponential growth of the fuzzy rule search space with the increase in the number of features considered in the learning process. In this work, we present a genetic feature selection process that can be integrated in a multistage genetic learning method to obtain, in a more efficient way, FRBCSs composed of a set of comprehensible fuzzy rules with high classification ability. The proposed process fixes, a priori, the number of selected features, and therefore, the size of the search space of candidate fuzzy rules. The experimentation carried out, using Sonar example base, shows a significant improvement on simplicity, precision and efficiency achieved by adding the proposed feature selection processes to the multistage genetic learning method or to other learning methods

Topics: Fuzzy Rule-Based Classication Systems, Inductive Learning, Feature Selection, Fuzzy Reasoning Methods
Year: 2000
OAI identifier: oai:CiteSeerX.psu:10.1.1.32.8843
Provided by: CiteSeerX
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