Comparative Analysis of Multi-Objective Feature Subset Selection using Meta-Heuristic Techniques

Abstract

ABSTRACT This paper presents a comparison of evolutionary algorithm based technique and swarm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large number of features called attributes. Some of these attributes are not significant and need to be removed. In the process of classification, a feature effects accuracy, cost and learning time of the classifier. So, before building a classifier there is a strong need to choose a subset of the attributes (features). This research treats feature subset selection as multi-objective optimization problem. The latest multi-objective techniques have been used for the comparison of evolutionary and swarm based algorithms. These techniques are Non-dominated Sorting Genetic Algorithms (NSGA -II) and Multiobjective Particle Swarm Optimization (MOPSO).MOPSO has also been converted into Binary MOPSO (BMOPSO) in order to deal with feature subset selection. The fitness value of a particular feature subset is measured by using ID3. The testing accuracy acquired is then assigned to the fitness value. The techniques are tested on several datasets taken from the UCI machine repository. The experiments demonstrate the feasibility of treating feature subset selection as multi-objective problem. NSGA-II has proved to be a better option for solving feature subset selection problem than BMOPSO