A Coevolutionary Approach to Optimize Class Boundaries for Multidimensional Classification Problems

Abstract

This paper proposes a coevolutionary classification method to discover classifiers for multidimensional pattern classification problems with continuous features. The classification problems may be decomposed into two sub-problems, which are feature selection and classifier adaptation. A coevolutionary classification method is designed by coordinating the two sub-problems, whose performances are affected by each other. The proposed method establishes a group of partial sub-regions, defined by regional feature set, and then fits a finite number of classifiers to the data pattern by combining a genetic algorithm and a local adaptation algorithm in every sub-region. A cycle of the cooperation loop is completed by evolving the sub-regions based on the evaluation results of the fitted classifiers located in the corresponding sub-regions. The classifier system has been tested with well-known data sets from the UCI machine-learning database, showing superior performance to other methods such as the nearest neighbor, decision tree, and neural networks.