Particle swarm optimisation for feature selection: A hybrid filter-wrapper approach
conference contributionposted on 24.03.2021, 01:43 by T Butler-Yeoman, Bing XueBing Xue, Mengjie ZhangMengjie Zhang
© 2015 IEEE. Feature selection is an important pre-processing step, which can reduce the dimensionality of a dataset and increase the accuracy and efficiency of a learning/classification algorithm. However, existing feature selection algorithms mainly wrappers and filters have their own advantages and disadvantages. This paper proposes two filter-wrapper hybrid feature selection algorithms based on particle swarm optimisation (PSO), where the first algorithm named FastPSO combined filter and wrapper into the search process of PSO for feature selection with most of the evaluations as filters and a small number of evaluations as wrappers. The second algorithm named RapidPSO further reduced the number of wrapper evaluations. Theoretical analysis on FastPSO and RapidPSO is conducted to investigate their complexity. FastPSO and RapidPSO are compared with a pure wrapper algorithm named WrapperPSO and a pure filter algorithm named FilterPSO on nine benchmark datasets of varying difficulty. The experimental results show that both FastPSO and RapidPSO can successfully reduce the number of features and simultaneously increase the classification performance over using all features. The two proposed algorithms maintain the high classification performance achieved by WrapperPSO and significantly reduce the computational time, although the number of features is larger. At the same time, they increase the classification accuracy of FilterPSO and reduce the number of features, but increased the computational cost. FastPSO outperformed RapidPSO in terms of the classification accuracy and the number of features, but increased the computational time, which shows the trade-off between the efficiency and effectiveness. © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.