I-mRMR: Incremental Max-Relevance, and Min-Redundancy Feature Selection

Yeliang Xiu; Suyun Zhao; Hong Chen; Cuiping Li

doi:10.1007/978-3-030-26075-0_8

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I-mRMR: Incremental Max-Relevance, and Min-Redundancy Feature Selection

Yeliang Xiu Suyun Zhao Hong Chen Cuiping Li

Year: 2019 Lecture notes in computer science Pages: 103-110 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-26075-0_8

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Keywords:

Computer science Minimum redundancy feature selection Redundancy (engineering) Feature selection Relevance (law) Data mining Computation Mutual information Artificial intelligence Selection (genetic algorithm) Pattern recognition (psychology) Set (abstract data type) Feature (linguistics) Key (lock) Machine learning Algorithm

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Data Mining Algorithms and Applications

Physical Sciences → Computer Science → Information Systems

Face and Expression Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Neural Networks and Applications

Physical Sciences → Computer Science → Artificial Intelligence

I-mRMR: Incremental Max-Relevance, and Min-Redundancy Feature Selection

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