Learning Discriminant Functions based on Genetic Programming and Rough Sets
Been-Chian Chien, Jui-Hsiang Yang and Tzung-Pei Hong
Supervised learning based on genetic programming can find different classification models including decision trees, classification rules and discriminant functions. The previous researches have shown that the classifiers learned by GP have high precision in many application domains. However, nominal data cannot be handled and calculated by the model of using discriminant functions. In this paper, we present a scheme based on rough set theory and genetic programming to learn discriminant functions from general data containing both nominal and numerical attributes. The proposed scheme first transforms the nominal data into numerical values by applying the technique of rough sets. Then, genetic programming is used to learn discriminant functions. The conflict problem among discriminant functions is solved by an effective conflict resolution method based on the distance-based fitness function. The experimental results show that the classifiers generated by the proposed scheme using GP are effective on nominal data in comparison with C4.5, CBA, and NB-based classifiers.
Keywords: Machine learning, discriminant function, genetic programming, classification, rough sets.