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Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems |
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| Authors: | Arfan Ali Nagra Qing Hua Ling Muhammad Abubaker Farooq Ahmad Sumet Mehta |
| Affiliation: | 1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu, People’s Republic of China;2. Jiangsu Key Laboratory of Security Technology for industrial Cyberspace, Zhenjiang, Jiangsu, People’s Republic of China;3. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, People’s Republic of China;4. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu, People’s Republic of China;5. Department of Computer science, COMSATS University, Lahore Campus, Lahore, Pakistan |
| Abstract: | ABSTRACTFeature 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. |
| Keywords: | Classification feature selection adaptive particle swarm optimisation gradient base local search inertia weight |