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A PSO Based Hybrid Feature Selection Algorithm For High-Dimensional Classification

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conference contribution
posted on 16.03.2021, 03:14 by BN Tran, Mengjie ZhangMengjie Zhang, Bing XueBing Xue
Recent research has shown that Particle Swarm Optimisation is a promising approach to feature selection. However, applying it on high-dimensional data with thousands to tens of thousands of features is still challenging because of the large search space. While filter approaches are time efficient and scalable for high-dimensional data, they usually obtain lower classification accuracy than wrapper approaches. On the other hand, wrapper methods require a longer running time than filter methods due to the learning algorithm involved in fitness evaluation. This paper proposes a new strategy of combining filter and wrapper approaches in a single evolutionary process in order to achieve smaller feature subsets with better classification performance in a shorter time. A new local search heuristic using symmetric uncertainty is proposed to refine the solutions found by PSO and a new hybrid fitness function is used to better evaluate candidate solutions. The proposed method is examined and compared with three recent PSO based methods on eight high-dimensional problems of varying difficulty. The results show that the new hybrid PSO is more effective and efficient than the other methods.

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Preferred citation

Tran, B. N., Zhang, M. & Xue, B. (2016, January). A PSO Based Hybrid Feature Selection Algorithm For High-Dimensional Classification. In Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC) 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada. IEEE. https://doi.org/10.1109/CEC.2016.7744271

Conference name

2016 IEEE Congress on Evolutionary Computation (CEC)

Conference Place

Vancouver, BC, Canada

Conference start date

24/07/2016

Conference finish date

29/07/2016

Title of proceedings

Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC)

Contribution type

Published Paper

Publication or Presentation Year

01/01/2016

Publisher

IEEE

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