Cascaded Two-Stage Feature Clustering and Selection via Separability and Consistency in Fuzzy Decision Systems

Feature selection is a vital technique in machine learning, as it can reduce computational complexity, improve model performance, and mitigate the risk of overfitting. However, the increasing complexity and dimensionality of datasets pose significant challenges in the selection of features. Focusing...

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Veröffentlicht in:	IEEE transactions on fuzzy systems 2024-09, Vol.32 (9), p.5320-5333
Hauptverfasser:	Chen, Yuepeng, Ding, Weiping, Ju, Hengrong, Huang, Jiashuang, Yin, Tao
Format:	Artikel
Sprache:	eng
Schlagworte:	Classification algorithms Clustering algorithms Feature extraction Feature selection fuzzy decision systems fuzzy neighborhood rough set (FNRS) Fuzzy systems granular computing Partitioning algorithms Rough sets Uncertainty
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