Fuzzy cost-based feature selection using interval multi-objective particle swarm optimization algorithm

Abstract

Cost-based feature selection is an important data preprocessing technique in classification problems. This paper focuses on a real case that the cost that may be associated with features is fuzzy number. First, a fuzzy transforming method is introduced to transform fuzzy cost-based feature selection problems into ones with interval number. Second, an effective feature selection algorithm based on interval multi-objective particle swarm optimization is proposed. In this algorithm, a risk coefficient that decision makers are willing to bear when delete any solution is used to update the archive. Also, an interval crowding distance measure is adopted to evaluate the distribution of non-dominated particles. Finally, feasibility of the presented algorithm is validated by simulation results. The results show that our algorithm is capable of generating excellent approximation of the true Pareto front.