Features Selection based on Rough Membership and Genetic Programming

This paper discusses the feature selection problem upon supervised learning. A learning method based on rough sets and genetic programming is proposed to select significant features and classify numerical data. The proposed method uses rough membership to transform nominal data into numerical values, then selects important features and learns classification functions using genetic programming. We use several UCI data sets to show the performance of the proposed scheme and make comparisons with three different features selection approaches: distance measure, information measure and dependence measure. The results demonstrate that the proposed method is effective both in features selection and classification.