HVS-MRMR wrapper method for variables selection

Abstract

This paper presents a new variable selection algorithm uses the heuristic variable selection (HVS) and Minimum Redundancy Maximum Relevance (MRMR). Our algorithm based on wrapper approach using multi-layer perceptron. We call this algorithm a HVS-MRMR Wrapper for variables selection. The relevance of a set of variables is measured by a convex combination of the relevance given by HVS criterion and the MRMR criterion. This approach selects new relevant variables. We evaluate the performance of HVS-MRMR on four benchmark classification problems. The experimental results show that HVS-MRMR selects a less number of variables with high classification accuracy compared to MRMR, HVS. HVS-MRMR can be applied to various classification problems requiring high classification accuracy.