首页|An evolutionary multiobjective method based on dominance and decomposition for feature selection in classification

An evolutionary multiobjective method based on dominance and decomposition for feature selection in classification

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Feature selection in classification can be considered a multiobjective problem with the objectives of increasing classification accuracy and decreasing the size of the selected feature subset.Dominance-based and decomposition-based multiobjective evolutionary algorithms(MOEAs)have been extensively used to address the feature selection problem due to their strong global search capability.However,most of them face the problem of not effectively balancing convergence and diversity during the evolutionary process.In addressing the aforementioned issue,this study proposes a unified evolutionary framework that combines two search forms of dominance and decomposition.The advantages of the two search methods assist one another in escaping the local optimum and inclining toward a balance of convergence and diversity.Specifi-cally,an improved environmental selection strategy based on the distributions of individuals in the objective space is presented to avoid duplicate feature subsets.Furthermore,a novel knowledge transfer mechanism that considers evolutionary characteristics is developed,allowing for the effective implementation of positive knowledge transfer between dominance-based and decomposition-based feature selection methods.The ex-perimental results demonstrate that the proposed algorithm can evolve feature subsets with good convergence and diversity in a shorter time compared with 9 state-of-the-art feature selection methods on 20 classification problems.

evolutionary algorithmsfeature selectionmultiobjective optimizationknowledge transferclassification

Jing LIANG、Yuyang ZHANG、Ke CHEN、Boyang QU、Kunjie YU、Caitong YUE、Ponnuthurai Nagaratnam SUGANTHAN

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School of Electrical and Information Engineering,Zhengzhou University,Zhengzhou 450001,China

State Key Laboratory of Intelligent Agricultural Power Equipment,Luoyang 471000,China

School of Electrical Engineering and Automation,Henan Institute of Technology,Xinxiang 453003,China

School of Electronics and Information,Zhongyuan University of Technology,Zhengzhou 450007,China

KINDI Center for Computing Research,College of Engineering,Qatar University,Doha 999043,Qatar

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National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Key R&D Program of ChinaNatural Science Foundation of Henan ProvinceProgram for Science & Technology Innovation Teams in Universities of Henan ProvinceProgram for Science & Technology Innovation Talents in Universities of Henan ProvinceChina Postdoctoral Science FoundationChina Postdoctoral Science FoundationChina Postdoctoral Science FoundationChina Postdoctoral Science Foundation

61876169619220726220625562176238621062302022YFD200120022230042008823IRTSTHN01023HASTIT0232022M7128782022TQ02982021T1406162021M692920

2024

10.1007/s11432-023-3864-6

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子：0.715
ISSN：1674-733X
年,卷(期)：2024.67(2)
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