A novel attribute reduction approach for multi-label data based on rough set theory

DOI10.1016/j.ins.2016.07.008zbMath1428.68243OpenAlexW2470167798MaRDI QIDQ2282044

Yan-Hui Zhai, Hua Li, De-Yu Li, Suge Wang, Jing Zhang

Publication date: 6 January 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2016.07.008

zbMATH Keywords

discernibility matrixrough set theorymulti-label classificationattribute reductcomplementary decision reduct

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (13)

Multi-label feature selection based on the division of label topics ⋮ Attribute reduction for multi-label classification based on labels of positive region ⋮ Label distribution feature selection for multi-label classification with rough set ⋮ Recent fuzzy generalisations of rough sets theory: a systematic review and methodological critique of the literature ⋮ Feature selection based on label distribution and fuzzy mutual information ⋮ Improving multi-label classification with missing labels by learning label-specific features ⋮ A three-way selective ensemble model for multi-label classification ⋮ Information structures in a fuzzy set-valued information system based on granular computing ⋮ A novel approach for learning label correlation with application to feature selection of multi-label data ⋮ A binary water wave optimization for feature selection ⋮ Attributes reduction algorithms for \(m\)-polar fuzzy relation decision systems ⋮ Multi-target support vector regression via correlation regressor chains ⋮ Local attribute reductions for decision tables

Uses Software

• BoosTexter
• LERS
• RoughSets

Cites Work

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