Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Nagra, Arfan Ali; Han, Fei; Ling, Qing Hua; Abubaker, Muhammad; Ahmad, Farooq; Mehta, Sumet; Apasiba, Abeo Timothy
• Is Part Of: Connection science. Volume 32:Issue 1(2020)
• Page Start: 16
• Page End: 36
• Publication Date: 2020-01-02
• Subjects: Classification -- feature selection -- adaptive particle swarm optimisation -- gradient base local search -- inertia weight; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2019.1609419
• Languages: English
• ISSNs: 0954-0091

Abstract

ABSTRACT: Feature selection is an important task to improve the classifier's accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.