An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature Selection

Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature selection (FS) methods is relatively simple, using only the Relief-F method to collect related features wi...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 27; H. 4; S. 1
Hauptverfasser:	Li, Lingjie, Xuan, Manlin, Lin, Qiuzhen, Jiang, Min, Ming, Zhong, Tan, Kay Chen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Classification competitive swarm optimizer Convergence evolutionary algorithm Evolutionary algorithms evolutionary multitasking Feature extraction Feature selection Filtering Filtration high-dimensional classification Knowledge management Knowledge transfer Multitasking Production methods Search problems Task analysis
ISSN:	1089-778X, 1941-0026
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Abstract	Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature selection (FS) methods is relatively simple, using only the Relief-F method to collect related features with similar importance into one task, which cannot provide more diversified tasks for knowledge transfer. Thus, this paper devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer to efficiently solve these related tasks via knowledge transfer. First, a diversified multiple task generation method is designed based on multiple filtering methods, which generates several relevant low-dimensional FS tasks by eliminating irrelevant features. In this way, useful knowledge for solving simple and relevant tasks can be transferred to simplify and speed up the solution of the original high-dimensional FS task. Then, a competitive swarm optimizer is modified to simultaneously solve these relevant FS tasks by transferring useful knowledge among them. Numerous empirical results demonstrate that the proposed EMT-based FS method can obtain a better feature subset than several state-of-the-art FS methods on eighteen high-dimensional datasets.
AbstractList	Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature selection (FS) methods is relatively simple, using only the Relief-[Formula Omitted] method to collect related features with similar importance into one task, which cannot provide more diversified tasks for knowledge transfer. Thus, this article devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer (CSO) to efficiently solve these related tasks via knowledge transfer. First, a diversified multiple task generation method is designed based on multiple filtering methods, which generates several relevant low-dimensional FS tasks by eliminating irrelevant features. In this way, useful knowledge for solving simple and relevant tasks can be transferred to simplify and speed up the solution of the original high-dimensional FS task. Then, a CSO is modified to simultaneously solve these relevant FS tasks by transferring useful knowledge among them. Numerous empirical results demonstrate that the proposed EMT-based FS method can obtain a better feature subset than several state-of-the-art FS methods on 18 high-dimensional datasets. Recently, evolutionary multitasking (EMT) has been successfully used in the field of high-dimensional classification. However, the generation of multiple tasks in the existing EMT-based feature selection (FS) methods is relatively simple, using only the Relief-F method to collect related features with similar importance into one task, which cannot provide more diversified tasks for knowledge transfer. Thus, this paper devises a new EMT algorithm for FS in high-dimensional classification, which first adopts different filtering methods to produce multiple tasks and then modifies a competitive swarm optimizer to efficiently solve these related tasks via knowledge transfer. First, a diversified multiple task generation method is designed based on multiple filtering methods, which generates several relevant low-dimensional FS tasks by eliminating irrelevant features. In this way, useful knowledge for solving simple and relevant tasks can be transferred to simplify and speed up the solution of the original high-dimensional FS task. Then, a competitive swarm optimizer is modified to simultaneously solve these relevant FS tasks by transferring useful knowledge among them. Numerous empirical results demonstrate that the proposed EMT-based FS method can obtain a better feature subset than several state-of-the-art FS methods on eighteen high-dimensional datasets.
Author	Tan, Kay Chen Xuan, Manlin Jiang, Min Li, Lingjie Ming, Zhong Lin, Qiuzhen
Author_xml	– sequence: 1 givenname: Lingjie surname: Li fullname: Li, Lingjie organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China – sequence: 2 givenname: Manlin surname: Xuan fullname: Xuan, Manlin organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China – sequence: 3 givenname: Qiuzhen orcidid: 0000-0003-2415-0401 surname: Lin fullname: Lin, Qiuzhen organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China – sequence: 4 givenname: Min orcidid: 0000-0003-2946-6974 surname: Jiang fullname: Jiang, Min organization: School of Informatics, Xiamen University, Xiamen, China – sequence: 5 givenname: Zhong orcidid: 0000-0001-9310-3460 surname: Ming fullname: Ming, Zhong organization: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China – sequence: 6 givenname: Kay Chen orcidid: 0000-0002-6802-2463 surname: Tan fullname: Tan, Kay Chen organization: Department of Computing, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
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SubjectTerms	Classification competitive swarm optimizer Convergence evolutionary algorithm Evolutionary algorithms evolutionary multitasking Feature extraction Feature selection Filtering Filtration high-dimensional classification Knowledge management Knowledge transfer Multitasking Production methods Search problems Task analysis
Title	An Evolutionary Multitasking Algorithm with Multiple Filtering for High-Dimensional Feature Selection
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