Multimodal particle swarm optimization for feature selection

The purpose of feature selection (FS) is to eliminate redundant and irrelevant features and leave useful features for classification, which can not only reduce the cost of classification, but also improve the classification accuracy. Existing algorithms mainly focus on finding one best feature subset for an optimization target or some Pareto solutions that best fit multiple targets, neglecting the fact that the FS problem may have more than one best feature subset for a single target. In fact, diffident feature subsets are likely to exhibit similar classification ability, so the FS problem is also a multimodal optimization problem. This paper firstly attempts to study the FS problem from the perspective of multimodal optimization. A novel multimodal niching particle swarm optimization (MNPSO) algorithm, aiming at finding out all the best feature combinations in a FS problem is proposed. Unlike traditional niching methods, the proposed algorithm uses the Hamming distance to measure the distance between any two particles. Two niching updating strategies are adopted for multimodal FS, and the two proposed variants of MNPSO are termed MNPSO-C (using crowding clustering) and MNPSO-S (using speciation clustering) respectively. To enable the particles in the same niche to exchange information properly, the particle velocity update is modified based on the best particle in the niche instead of the traditional globally best one. An external archive is applied to store the feature subsets with the highest classification accuracy. Datasets with various dimensions of attributes have been tested. Particularly, the number of multimodal solutions and the successful rates of the proposed algorithms have been extensively analyzed and compared with the state-of-the-art algorithms. The experimental results show that the proposed algorithms can find more multimodal feature solutions and have advantages in classification accuracy. (C) 2021 Elsevier B.V. All rights reserved.