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Adaptive feature selection using v-shaped binary particle swarm optimization

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  • Xuyang Teng
  • Hongbin Dong
  • Xiurong Zhou

Abstract

Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers.

Suggested Citation

  • Xuyang Teng & Hongbin Dong & Xiurong Zhou, 2017. "Adaptive feature selection using v-shaped binary particle swarm optimization," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-22, March.
  • Handle: RePEc:plo:pone00:0173907
    DOI: 10.1371/journal.pone.0173907
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    References listed on IDEAS

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    1. Othman Soufan & Dimitrios Kleftogiannis & Panos Kalnis & Vladimir B Bajic, 2015. "DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-23, February.
    2. Du, Wen-Bo & Gao, Yang & Liu, Chen & Zheng, Zheng & Wang, Zhen, 2015. "Adequate is better: particle swarm optimization with limited-information," Applied Mathematics and Computation, Elsevier, vol. 268(C), pages 832-838.
    3. Taqwa Ahmed Alhaj & Maheyzah Md Siraj & Anazida Zainal & Huwaida Tagelsir Elshoush & Fatin Elhaj, 2016. "Feature Selection Using Information Gain for Improved Structural-Based Alert Correlation," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-18, November.
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    Cited by:

    1. Mona A. S. Ali & Fathimathul Rajeena P. P. & Diaa Salama Abd Elminaam, 2022. "An Efficient Heap Based Optimizer Algorithm for Feature Selection," Mathematics, MDPI, vol. 10(14), pages 1-33, July.

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