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Global chaotic bat algorithm for feature selection

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Abstract

The wrapper algorithm adopts the performance of the learning algorithm as the evaluation criteria to obtain excellent classification performance. However, the wrapper algorithm is prone to converge prematurely. A global chaotic bat algorithm (GCBA) is put up forward to improve this shortage. First, GCBA applies chaotic map to population initialization to cover the entire solution space. In addition, adaptive learning factors are presented to balance exploration and exploration. The learning factor of local optimal position gradually decreases in the early stage while the learning factor of global optimal position gradually increases in the later stage. Finally, to improve the exploitation, an improved transfer function is proposed, which transfers the continuous space to discrete binary space. GCBA is tested on 14 UCI data sets and 5 gene expression data sets compared with other 6 comparison algorithms. Compared with other algorithms, the results show that GCBA is able to achieve better classification performance.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, China

    Ying Li, Xueting Cui & Jiahao Fan

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, China

    Ying Li, Xueting Cui & Jiahao Fan

  3. Northeast Asian Research Center, Jilin University, Changchun, China

    Tan Wang

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  1. Ying Li
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  2. Xueting Cui
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  3. Jiahao Fan
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Correspondence to Jiahao Fan.

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Li, Y., Cui, X., Fan, J. et al. Global chaotic bat algorithm for feature selection. J Supercomput 78, 18754–18776 (2022). https://doi.org/10.1007/s11227-022-04606-0

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