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Multi-label feature selection via redundancy of the selected feature set

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Abstract

Due to the growing number of high-dimensional multi-label data which emerge in modern applications, multi-label feature selection becomes an important issue. Traditional multi-label feature selection algorithms focus on evaluating the relevance of the candidate and label set, which neglects the impact of selected feature set. There are few studies on the redundancy analysis of the selected features, resulting in the most discriminative features being ignored. To solve this problem, we propose a novel multi-label feature selection algorithm based on the fuzzy rough set. First, we propose the definition of redundancy weight for each selected feature via fuzzy interaction information to evaluate the correlation between features in selected feature set, and design the instance equivalence matrix based on the redundancy weight. Second, the fuzzy conditional mutual information is defined to evaluate the relevance between candidate features and label set given selected feature set. Finally, we combine the redundancy analysis with the feature relevance for designing the multi-label feature selection algorithm. To verify the performance of the proposed algorithm, the proposed algorithm is compared to nine representative feature selection algorithms on synthetic and real-world datasets. The experimental test and statistical test show that our proposed algorithm outperforms the other compared algorithms.

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Acknowledgements

This work was supported by The National Nature Science Foundation of China (Grant Nos. , 61772226 and 61862056), The Natural Science Foundation of Jilin Province (Grant number No. 20200201159JC), Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China, Jilin University.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Haibo Zhong, Ping Zhang & Guixia Liu

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Haibo Zhong, Ping Zhang & Guixia Liu

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  1. Haibo Zhong
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  2. Ping Zhang
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  3. Guixia Liu
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Correspondence to Guixia Liu.

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Zhong, H., Zhang, P. & Liu, G. Multi-label feature selection via redundancy of the selected feature set. Appl Intell 53, 11073–11091 (2023). https://doi.org/10.1007/s10489-022-03365-y

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