Clustering-Guided Particle Swarm Feature Selection Algorithm for High-Dimensional Imbalanced Data With Missing Values

Feature selection (FS) in data with class imbalance or missing values has received much attention from researchers due to their universality in real-world applications. However, for data with both the two characteristics above, there is still a lack of the corresponding FS algorithm. Due to the comp...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2022-08, Vol.26 (4), p.616-630
Main Authors: Zhang, Yong, Wang, Yan-Hu, Gong, Dun-Wei, Sun, Xiao-Yan
Format: Article
Language:English
Subjects:
Algorithms
Class imbalance
Clustering
Clustering algorithms
Computational efficiency
Feature extraction
Feature selection
feature selection (FS)
fuzzy clustering
Missing data
missing value
Mutual information
Operators (mathematics)
Particle swarm optimization
particle swarm optimization (PSO)
Performance enhancement
Search problems
Sun
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Summary:Feature selection (FS) in data with class imbalance or missing values has received much attention from researchers due to their universality in real-world applications. However, for data with both the two characteristics above, there is still a lack of the corresponding FS algorithm. Due to the complex coupling relationship between missing data and class imbalance, the need for better FS method becomes essential. To tackle high-dimensional imbalanced data with missing values, this article studies a new evolutionary FS method. First, an improved F -measure based on filling risk (RF-measure) is defined to evaluate the influence of missing data on the performance of FS in the case of class imbalance. Following that taking the RF-measure as an objective function, a particle swarm optimization-based FS method with fuzzy clustering (PSOFS-FC) is proposed. Two new problem-specific operators or strategies, i.e., the swarm initialization strategy guided by fuzzy clustering and the local pruning operator based on feature importance, are developed to improve the performance of PSOFS-FC. Compared with state-of-the-art FS algorithms on several public datasets, experimental results show that PSOFS-FC can achieve excellent classification performance with relatively less running time, indicating its superiority on tackling high-dimensional imbalanced data with missing values.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2021.3106975