Variable-Size Cooperative Coevolutionary Particle Swarm Optimization for Feature Selection on High-Dimensional Data

Evolutionary feature selection (FS) methods face the challenge of "curse of dimensionality" when dealing with high-dimensional data. Focusing on this challenge, this article studies a variable-size cooperative coevolutionary particle swarm optimization algorithm (VS-CCPSO) for FS. The prop...

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Vydané v:IEEE transactions on evolutionary computation Ročník 24; číslo 5; s. 882 - 895
Hlavní autori: Song, Xian-Fang, Zhang, Yong, Guo, Yi-Nan, Sun, Xiao-Yan, Wang, Yong-Li
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Clustering
Computational efficiency
Computing costs
Convergence
Cooperative coevolutionary (CC)
Correlation
Feature extraction
Feature selection
feature selection (FS)
Particle swarm optimization
particle swarm optimization (PSO)
Search problems
Strategy
Uncertainty
variable-population
ISSN:1089-778X, 1941-0026
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Shrnutí:Evolutionary feature selection (FS) methods face the challenge of "curse of dimensionality" when dealing with high-dimensional data. Focusing on this challenge, this article studies a variable-size cooperative coevolutionary particle swarm optimization algorithm (VS-CCPSO) for FS. The proposed algorithm employs the idea of "divide and conquer" in cooperative coevolutionary approach, but several new developed problem-guided operators/strategies make it more suitable for FS problems. First, a space division strategy based on the feature importance is presented, which can classify relevant features into the same subspace with a low computational cost. Following that, an adaptive adjustment mechanism of subswarm size is developed to maintain an appropriate size for each subswarm, with the purpose of saving computational cost on evaluating particles. Moreover, a particle deletion strategy based on fitness-guided binary clustering, and a particle generation strategy based on feature importance and crossover both are designed to ensure the quality of particles in the subswarms. We apply VS-CCPSO to 12 typical datasets and compare it with six state-of-the-art methods. The experimental results show that VS-CCPSO has the capability of obtaining good feature subsets, suggesting its competitiveness for tackling FS problems with high dimensionality.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2020.2968743