Feature selection using multimodal optimization techniques

Abstract

This paper investigates the effect of using Multimodal Optimization (MO) techniques on solving the Feature Selection (FSel) problem. The FSel problem is a high-dimensional optimization problem in the nature and thus needs a solver with high exploration power. On the other hand, if alternative optimal solutions could be provided for a problem, the implementation phase may become more selective depending on the cost and limitations of domain of the problem. The high exploration power and solution conservation capability of MO methods make them able to find multiple suitable solutions in a single run. Therefore, MO methods can be considered as a powerful tool of finding suitable feature subsets for FSel problem. In this paper, we made a special study on the use of MO methods in the feature selection problem. The binary versions of some existing Evolutionary Algorithm (EA) based MO methods like Dynamic Fitness Sharing (DFS), local Best PSO variants and GA_SN_CM, are proposed and used for selection of suitable features from several benchmark datasets. The results obtained by the MO methods are compared to some well-known heuristic approaches for FSel problem from the literature. The obtained results and their statistical analyses indicate the effectiveness of MO methods in finding multiple accurate feature subsets compared to existing powerful methods.