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

粒子群优化 特征选择 计算机科学 数学优化 进化计算 选择(遗传算法) 人工智能 变量(数学) 模式识别(心理学) 特征(语言学) 数学 算法 数学分析 哲学 语言学
作者
Xianfang Song,Zhang Yon,Yinan Guo,Xiaoyan Sun,Yongli Wang
出处
期刊:IEEE Transactions on Evolutionary Computation [Institute of Electrical and Electronics Engineers]
卷期号:24 (5): 882-895 被引量:287
标识
DOI:10.1109/tevc.2020.2968743
摘要

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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