Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (7): 195-204.doi: 10.11896/jsjkx.250500032

• Artificial Intelligence • Previous Articles     Next Articles

Feature Selection Based on Hierarchical Quantum Bat Algorithm

ZHANG Xingwang1, HE Xiaoli2,3, CHEN Si2, SHE Yanhong2,3   

  1. 1 School of Computer Science,Xi'an Shiyou University,Xi'an 710065,China
    2 School of Science,Xi'an Shiyou University,Xi'an 710065,China
    3 Center for Conceptual Cognition and Intelligence,Northwest University,Xi'an 710127,China
  • Received:2025-05-12 Revised:2025-09-12 Online:2026-07-15 Published:2026-07-10
  • About author:ZHANG Xingwang,born in 2001,postgraduate.His main research interests include swarm intelligence algorithms and their applications,feature selection.
    HE Xiaoli,born in 1982,Ph.D,associate professor.Her main research interests include uncertainty reasoning and gra-nularity computation.
  • Supported by:
    National Natural Science Foundation of China(12471442,12001422,61976244,12171388),Natural Science Basic Research Program of Shaanxi Province(2023-JC-YB-027,2025JC-YBQN-102,2025JC-YBMS-034),General Project of Humanities and Social Sciences Research of Ministry of Education(24XJC72040001),Shaanxi Basic Research Project of Mathematics and Physics(23JSQ047) and Youth Innovation Team Project of Shaanxi Provincial Department of Education(23JP130,23JP132).

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Abstract: Feature selection(FS),as a core step in pattern recognition,aims to optimize classification performance and computational efficiency through dimensionality reduction.To address the challenges faced by existing swarm intelligence algorithms,such as the BA(Bat Algorithm),in high-dimensional data,including low search efficiency and susceptibility to local optima,a IRHQBA(Hierarchical Quantum Bat Algorithm Based on Information Gain Ratio and Random Forest) is designed.Firstly,a hybrid filter-based pre-selection mechanism is constructed,integrating Pearson correlation,IGR(Information Gain Ratio),and RF(Random Forest) for triple evaluation,to efficiently eliminate redundant features.Secondly,in the BA initialization stage,hierarchical sub-feature grouping is conducted based on feature importance ranking,and quantum computing is introduced to optimize the search space and mutation strategy to enhance diversity.Finally,the classification performance of feature subsets is improved through hierarchical collaborative optimization and dynamic mutation mechanisms.

Key words: Feature selection, Bat algorithm, Quantum computing, Feature importance, Classification

CLC Number: 

  • TP311.13
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