Feature Selection for Multi-Label Learning Based on F-Neighborhood Rough Sets

Multi-label learning is often applied to handle complex decision tasks, and feature selection is its essential part. The relation of labels is always ignored or not enough to consider for both multi-label learning and its feature selection. To deal with the problem, F-neighborhood rough sets are emp...

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Bibliografische Detailangaben

Hauptverfasser:	Zhixuan Deng, Zhonglong Zheng, Dayong Deng, Tianxiang Wang, Yiran He, Dawei Zhang
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IEEE 2020-01-01
Schriftenreihe:	IEEE Access
Schlagworte:	Rough sets feature selection multi-label learning F-neighborhood rough sets attribute significance matrix
Online-Zugang:	https://ieeexplore.ieee.org/document/9007716/
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