Semi-supervised feature selection via fuzzy C-means clustering and simulated annealing optimization

Abstract Feature selection is a critical step in machine learning, as it helps minimize redundant features, thereby enhancing model performance and interpretability. In practice, partially labeled datasets are common. When some data labels are unavailable, semi-supervised feature selection has becom...

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Bibliografische gegevens

Hoofdauteurs:	Hongwu Qin, An Gao, Xiuqin Ma, Keqi Cheng
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	Springer 2026-02-01
Reeks:	Journal of King Saud University: Computer and Information Sciences
Onderwerpen:	Feature selection semi-supervised Fuzzy C-means clustering Simulated annealing
Online toegang:	https://doi.org/10.1007/s44443-026-00502-2
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