Balancing Different Optimization Difficulty Between Objectives in Multiobjective Feature Selection

Zhenshou Song, Handing Wang, Bing Xue, Mengjie Zhang

Abstract: Multiobjective feature selection (MOFS) aims to find a set of feature subsets that achieves a tradeoff between two objectives, i.e., reducing the number of selected features and improving the classification performance. However, these two objectives might not be always conflicting during the optimization process and have varying difficulties in optimization. Such characteristics pose a great challenge to existing multiobjective evolutionary approaches, which often treat two objectives equally. Specifically, a large number of feature subsets with few features may appear in the population and compete for survival opportunities with promising feature subsets located in not fully explored regions, leading to poor performance. To this end, we propose a two-archive evolutionary feature selection algorithm for MOFS. In the proposed method, all individuals are equally allocated into two independent archives. A two-archive-based solution generation strategy is proposed, where a dynamic dimensionality reduction operator is used to exploit small features subsets while a diversity-based mutation operator is utilized to find feature subsets with better-classification performance. Moreover, a novel environmental selection scheme is proposed, which aims to improve the survival probability of promising feature subsets by providing different selection environments. Experimental results on 23 datasets demonstrate that the proposed algorithm is superior to the other five state-of-the-art algorithms.