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Multi-label learning with Relief-based label-specific feature selection

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Abstract

Multi-label learning is an emerging paradigm exploiting samples with rich semantics. As an effective solution to multi-label learning, the strategy of label-specific features (LIFT) has been widely applied. Technically, such strategy feeds the tailored features to learning model instead of the original ones. However, tailoring features for each label may cause redundancy or irrelevance in feature space, thereby deteriorating the learning performance. To alleviate such a problem, a novel multi-label classification method named Relief-LIFT is proposed in this study. Relief-LIFT firstly leverages LIFT to generate the toiled features, and then adjusts Relief to select informative features from those toiled ones for the classification model. Experimental results on 12 real-world multi-label data sets demonstrate that, our proposed Relief-LIFT can achieve better performance as compared with other well-established multi-label classification methods.

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Data Availability

The image data generated or analysed during this study is included in this published article [Refer: M.L. Zhang, Z.H. Zhou, ML-kNN: A lazy learning approach to multi-label learning, Pattern Recognition, 40, 2038-2048 (2007)]. The remaining data sets generated during or analysed during the current study are available in the Mulan repository, [http://mulan.sourceforge.net/index.html].

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (62076111, 62006128, 62006099).

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Authors and Affiliations

  1. School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212100, Jiangsu, China

    Jiadong Zhang, Keyu Liu & Xibei Yang

  2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, Sichuan, China

    Keyu Liu

  3. Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan, 316022, Zhejiang, China

    Xibei Yang

  4. School of Information Science and Technology, Nantong University, Nantong, 226000, Jiangsu, China

    Hengrong Ju

  5. Department of Computer Science and Technology, Nanjing University, Nanjing, 210023, Jiangsu, China

    Suping Xu

  6. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6R 2V4, AB, Canada

    Suping Xu

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  1. Jiadong Zhang
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  2. Keyu Liu
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Correspondence to Keyu Liu.

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Zhang, J., Liu, K., Yang, X. et al. Multi-label learning with Relief-based label-specific feature selection. Appl Intell 53, 18517–18530 (2023). https://doi.org/10.1007/s10489-022-04350-1

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