Feature Selection With Maximal Relevance and Minimal Supervised Redundancy-Reference-Cited by-同舟云学术

Feature Selection With Maximal Relevance and Minimal Supervised Redundancy

Published:2023-02 Issue:2 Volume:53 Page:707-717
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Wang Yadi¹^ORCID,Li Xiaoping²^ORCID,Ruiz Ruben³^ORCID

Affiliation:

1. Henan Key Laboratory of Big Data Analysis and Processing and the Institute of Data and Knowledge Engineering, School of Computer and Information Engineering, Henan University, Kaifeng, China

2. School of Computer Science and Engineering and the Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing, China

3. Grupo de Sistemas de Optimización Aplicada, Instituto Tecnológico de Informática, Ciudad Politécnica de la Innovación, Universitat Politècnica de València, València, Spain

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Collaborative Innovation Center of Wireless Communications Technology and Key Research Projects of Henan Higher Education Institutions

Key Research and Promotion Projects of Henan Province

Spanish Ministry of Science, Innovation, and Universities through the Project “OPTEP-Port Terminal Operations Optimization” financed with FEDER Funds

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/10016773/09706417.pdf?arnumber=9706417

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5. Information-Theoretic Feature Selection in Microarray Data Using Variable Complementarity

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