On some aspects of minimum redundancy maximum relevance feature selection

Peter Bugata; Peter Drotár

doi:10.1007/s11432-019-2633-y

ScienceGate Book Chapters

JOURNAL ARTICLE

On some aspects of minimum redundancy maximum relevance feature selection

Peter Bugata Peter Drotár

Year: 2019 Journal: Science China Information Sciences Vol: 63 (1) Publisher: Springer Nature

DOI: 10.1007/s11432-019-2633-y

Get Full-Text PDF Get Analytical Report

Keywords:

Minimum redundancy feature selection Feature selection Redundancy (engineering) Dependency (UML) Artificial intelligence Feature (linguistics) Computer science Pattern recognition (psychology) Relevance (law) Selection (genetic algorithm) Machine learning Data mining

Metrics

Cited By

2.99

FWCI (Field Weighted Citation Impact)

Refs

0.90

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Gene expression and cancer classification

Life Sciences → Biochemistry, Genetics and Molecular Biology → Molecular Biology

Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Neural Networks and Applications

Physical Sciences → Computer Science → Artificial Intelligence

On some aspects of minimum redundancy maximum relevance feature selection

Metrics

Citation History

Topics

Related Documents

Unsupervised Minimum Redundancy Maximum Relevance Feature Selection for Predictive Maintenance

Maximum relevance minimum common redundancy feature selection for nonlinear data

Unsupervised feature selection via maximum relevance and minimum global redundancy

Semi-supervised minimum redundancy maximum relevance feature selection for audio classification

Minimum redundancy maximum relevance feature selection approach for temporal gene expression data