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Feature selection for multi-label learning with missing labels

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Abstract

In multi-label learning, feature selection is a non-ignorable preprocessing step which can alleviate the negative effect of high-dimensionality. To address this problem, a number of effective information theory based feature selection algorithms for multi-label learning are proposed. However, these existing algorithms assume that the label space of multi-label training data is complete. In practice, the standpoint does not always hold true, due to the ambiguity among class labels or the cost effort to fully annotate instances. In this paper, we first define the new concepts of multi-label information entropy and multi-label mutual information. Then, feature redundancy, feature independence, and feature interaction are defined, respectively. In which, feature interaction is used to select more valuable features which may be ignored due to the incomplete label space. Moreover, a multi-label feature selection method with missing labels is proposed. Finally, extensive experiments conducted on eight publicly available data sets verify the effectiveness of the proposed algorithm via comparing it with state-of-the-art methods.

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Acknowledgments

The authors would like to thank the anonymous reviewers and the editor for their constructive and valuable comments. This work is supported by Grants from the National Natural Science Foundation of China (No. 61672272), the Natural Science Foundation of Fujian Province (Nos. 2018J01548, 2016J01314, and 2018J01547), and the Department of Education of Fujian Province (No. JT180318).

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Authors and Affiliations

  1. School of Computer Science, Minnan Normal University, Zhangzhou, 363000, People’s Republic of China

    Chenxi Wang & Yaojin Lin

  2. Key Laboratory of Data Science and Intelligence Application, Fujian Province University, Fuzhou, People’s Republic of China

    Chenxi Wang, Yaojin Lin & Jinghua Liu

  3. Department of Automation, Xiamen University, Xiamen, 361000, People’s Republic of China

    Jinghua Liu

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  1. Chenxi Wang
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  2. Yaojin Lin
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  3. Jinghua Liu
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Correspondence to Chenxi Wang or Yaojin Lin.

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Wang, C., Lin, Y. & Liu, J. Feature selection for multi-label learning with missing labels. Appl Intell 49, 3027–3042 (2019). https://doi.org/10.1007/s10489-019-01431-6

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