Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

• Published in: Connection science Vol. 32; no. 1; pp. 16 - 36
• Main Authors: Nagra, Arfan Ali, Han, Fei, Ling, Qing Hua, Abubaker, Muhammad, Ahmad, Farooq, Mehta, Sumet, Apasiba, Abeo Timothy
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.01.2020; Taylor & Francis Ltd
• Subjects: adaptive particle swarm optimisation; Algorithms; Classification; Classifiers; Decision trees; feature selection; gradient base local search; Heuristic methods; Inertia; inertia weight; Particle swarm optimization; Searching; Standard data; Weight
• ISSN: 0954-0091; 1360-0494
• DOI: 10.1080/09540091.2019.1609419

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.