A Variable Granularity Search-Based Multiobjective Feature Selection Algorithm for High-Dimensional Data Classification-Reference-Cited by-同舟云学术

A Variable Granularity Search-Based Multiobjective Feature Selection Algorithm for High-Dimensional Data Classification

Published:2023-04 Issue:2 Volume:27 Page:266-280
ISSN:1089-778X
Container-title:IEEE Transactions on Evolutionary Computation
language:
Short-container-title:IEEE Trans. Evol. Computat.

Author:

Cheng Fan¹,Cui Junjie¹,Wang Qijun¹,Zhang Lei¹^ORCID

Affiliation:

1. Information Materials and Intelligent Sensing Laboratory of Anhui Province, Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, China

Funder

National Natural Science Foundation of China

Key Projects of University Excellent Talents Support Plan of Anhui Provincial Department of Education

Natural Science Foundation of Anhui Province

Humanities and Social Sciences Project of Chinese Ministry of Education

University Synergy Innovation Program of Anhui Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Theoretical Computer Science,Software

Link

http://xplorestaging.ieee.org/ielx7/4235/10089199/09737335.pdf?arnumber=9737335

Reference45 articles.

1. A new and fast rival genetic algorithm for feature selection

2. Pareto front feature selection based on artificial bee colony optimization

3. A genetic algorithm-based method for feature subset selection

4. Multi-objective PSO based online feature selection for multi-label classification

5. Feature selection algorithm based on bare bones particle swarm optimization

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