A multi-objective evolutionary algorithm for feature selection based on mutual information with a new redundancy measure

DOI10.1016/j.ins.2015.02.031zbMath1387.68200OpenAlexW2063910191MaRDI QIDQ1749103

Juanzi Li, Minqiang Li, Zhichun Wang

Publication date: 15 May 2018

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

Full work available at URL: https://doi.org/10.1016/j.ins.2015.02.031

zbMATH Keywords

mutual informationdata miningfeature selectionmulti-objective evolutionary algorithm

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items

Multi-variable regression methods using modified Chebyshev polynomials of class 2 ⋮ Semi-supervised sparse feature selection via graph Laplacian based scatter matrix for regression problems ⋮ Membership-margin based feature selection for mixed type and high-dimensional data: theory and applications ⋮ Image based techniques for crack detection, classification and quantification in asphalt pavement: a review ⋮ Maximum relevance minimum common redundancy feature selection for nonlinear data

Uses Software

• DistAl
• UCI-ml
• SPEA2
• C4.5

Cites Work

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• A novel feature selection approach: combining feature wrappers and filters
• On the handling of continuous-valued attributes in decision tree generation
• Independent component analysis, a new concept?
• 10.1162/153244303322753616