Ensemble feature selection using election methods and ranker clustering

DOI10.1016/j.ins.2018.12.033zbMath1443.68146OpenAlexW2906662890MaRDI QIDQ2004741

Peter Drotár, Matej Gazda, Liberios Vokorokos

Publication date: 7 October 2020

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

Full work available at URL: https://doi.org/10.1016/j.ins.2018.12.033

zbMATH Keywords

votingfeature selectionensembleBorda countmean-shift clustering

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Voting theory (91B12)

Related Items (3)

Adaptive discriminant analysis for semi-supervised feature selection ⋮ Approximate Condorcet partitioning: solving large-scale rank aggregation problems ⋮ A distributed networked system for secure publicly verifiable self-tallying online voting

Uses Software

• Scikit
• AdaBoost-SAMME
• ReliefF

Cites Work

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• Multi-class AdaBoost
• Wrappers for feature subset selection
• Single transferable vote resists strategic voting
• Theoretical and empirical analysis of ReliefF and RReliefF
• Breaking the Curse of Dimensionality with Convex Neural Networks

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