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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Zusammenfassung: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.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2023.3254155