A Many-Objective Diversity-Guided Differential Evolution Algorithm for Multi-Label Feature Selection in High-Dimensional Datasets

Multi-label classification (MLC) is crucial as it allows for a more nuanced and realistic representation of complex real-world scenarios, where instances may belong to multiple categories simultaneously, providing a comprehensive understanding of the data. Effective feature selection in MLC is param...

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Bibliographic Details
Published in:	IEEE transactions on emerging topics in computational intelligence Vol. 9; no. 2; pp. 1226 - 1237
Main Authors:	Hancer, Emrah, Xue, Bing, Zhang, Mengjie
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.04.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Algorithms Classification algorithms Computational modeling Convergence Datasets differential evolution Effectiveness Evolutionary algorithms Evolutionary computation Feature extraction Feature selection Labels Linear programming multi-label classification Multi-label feature selection Multiple objective analysis Optimization Search problems Vectors
ISSN:	2471-285X, 2471-285X
Online Access:	Get full text
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