Investigation on particle swarm optimisation for feature selection on high-dimensional data: local search and selection bias. Issue 3 (2nd July 2016)

• Record Type: Journal Article
• Authors: Tran, Binh; Xue, Bing; Zhang, Mengjie; Nguyen, Su
• Is Part Of: Connection science. Volume 28:Issue 3(2016)
• Page Start: 270
• Page End: 294
• Publication Date: 2016-07-02
• Subjects: Feature selection -- particle swarm optimisation -- high-dimensional data -- classification; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2016.1185392
• Languages: English
• ISSNs: 0954-0091

Abstract

ABSTRACT: Feature selection is an essential step in classification tasks with a large number of features, such as in gene expression data. Recent research has shown that particle swarm optimisation (PSO) is a promising approach to feature selection. However, it also has potential limitation to get stuck into local optima, especially for gene selection problems with a huge search space. Therefore, we developed a PSO algorithm (PSO-LSRG) with a fast "local search" combined with a gbest resetting mechanism as a way to improve the performance of PSO for feature selection. Furthermore, since many existing PSO-based feature selection approaches on the gene expression data have feature selection bias, i.e. no unseen test data is used, 2 sets of experiments on 10 gene expression datasets were designed: with and without feature selection bias. As compared to standard PSO, PSO with gbest resetting only, and PSO with local search only, PSO-LSRG obtained a substantial dimensionality reduction and a significant improvement on the classification performance in both sets of experiments. PSO-LSRG outperforms the other three algorithms when feature selection bias exists. When there is no feature selection bias, PSO-LSRG selects the smallest number of features in all cases, but the classification performance is slightly worse in a few cases, which may be caused by the overfitting problem. This shows that feature selection bias should be avoided when designing a feature selection algorithm to ensure its generalisation ability on unseen data.