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Margin attribute reductions for multi-label classification

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Abstract

Multi-label classification is a typical supervised machine learning problem and widely applied in text classification and image recognition. When there are redundant attributes in the data, the efficiency of classification will be reduced. However, the existing attribute reduction algorithms have high computational complexity. This paper aims to design an efficient attribute reduction algorithm. The k pairs of boundary samples were selected from the positive and negative classes respectively, and the distance between each pair was calculated as the evaluation of attributes. By maximizing the evaluation function, the definition of reduction and the design of the algorithm were established. The comparison experiment is carried out on eight generic multi-label data. The experimental results show that the attribute importance evaluation defined in this paper can better represent the classification performance of the attribute for multi-label classification. The boundary samples can better reflect the classification effect of attributes. The proposed model avoids the point-by-point statistics of all samples’ information and improves the computational efficiency.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61976027, Liaoning Provincial Department of Education under Grant LJ2019011, Liaoning Natural Foundation Guidance Plan under Grant 2019-ZD-0502, and Liaoning Revitalization Talents Program under Grant XLYC2008002.

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Authors and Affiliations

  1. Department of Mathematics, Bohai University, Jinzhou, 121013, China

    Xiaodong Fan, Xiangyue Chen, Changzhong Wang, Yang Wang & Ying Zhang

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  1. Xiaodong Fan
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  2. Xiangyue Chen
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  3. Changzhong Wang
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  4. Yang Wang
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  5. Ying Zhang
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Correspondence to Xiaodong Fan.

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Fan, X., Chen, X., Wang, C. et al. Margin attribute reductions for multi-label classification. Appl Intell 52, 6079–6092 (2022). https://doi.org/10.1007/s10489-021-02740-5

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