A Classifier Learning Scheme based on Rough Membership and Genetic Programming

Abstract

Classification is one of the important research topics in knowledge discovery and machine learning. A new classifier learning method using genetic programming has been developed for classifying numerical data recently. However, it is difficult for a function-based classifier to classify general nominal data, because nominal data may have possible large distinct values with no ordering values. In this paper, we present a new scheme based on rough set theory and genetic programming to learn a classifier from the data with both nominal and numerical attributes. The proposed scheme first transforms the nominal data into numerical values by rough membership functions. The classification functions then can be generated by genetic programming easily. We use several URI data sets to show the performance of the proposed scheme and make comparisons with other methods.