计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (7): 195-204.doi: 10.11896/jsjkx.250500032

• 人工智能 • 上一篇    下一篇

基于层次化量子蝙蝠算法的特征选择

张兴旺1, 贺晓丽2,3, 陈思2, 折延宏2,3   

  1. 1 西安石油大学计算机学院 西安 710065
    2 西安石油大学理学院 西安 710065
    3 西北大学概念认知与智能研究中心 西安 710127
  • 收稿日期:2025-05-12 修回日期:2025-09-12 出版日期:2026-07-15 发布日期:2026-07-10
  • 通讯作者: 贺晓丽(hexl@xsyu.edu.cn)
  • 作者简介:(23211060848@stumail.xsyu.edu.cn)
  • 基金资助:
    国家自然科学基金(12471442,12001422,61976244,12171388);陕西省自然科学基础研究计划(2023-JC-YB-027,2025JC-YBQN-102,2025JC-YBMS-034);教育部人文社会科学研究一般项目(24XJC72040001);陕西数理基础科学研究项目(23JSQ047);陕西省教育厅高校青年创新团队项目(23JP130,23JP132)

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 Published:2026-07-15 Online: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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摘要： 特征选择(FS)作为模式识别的核心步骤,旨在通过降维优化分类性能与计算效率。针对现有群体智能算法,如蝙蝠算法(BA)在高维数据中面临的搜索效率低、易陷入局部最优等挑战,设计了一种基于信息增益率和随机森林的层次化量子蝙蝠算法(Hierarchical Quantum Bat Algorithm Based on Information Gain Ratio and Random Forest,IRHQBA)。首先构建混合过滤式预选机制,融合Pearson相关性、信息增益率(IGR)与随机森林(RF)三重评估,高效剔除冗余特征;其次,在BA初始化阶段,基于特征重要性排序进行分层子特征分组,并引入量子计算优化搜索空间与突变策略增强多样性;最后,通过分层协同优化与动态突变机制提升特征子集的分类性能。

关键词: 特征选择, 蝙蝠算法, 量子计算, 特征重要性, 分类

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

中图分类号: 

  • TP311.13
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