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Clustering-Guided Particle Swarm Feature Selection Algorithm for High-Dimensional Imbalanced Data With Missing Values | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

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. H...Show More

Abstract:

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.
Published in: IEEE Transactions on Evolutionary Computation ( Volume: 26, Issue: 4, August 2022)
Page(s): 616 - 630
Date of Publication: 24 August 2021

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