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Multi-label feature selection based on information entropy fusion in multi-source decision system

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Abstract

Feature selection plays an important role in high-dimensional multi-source data, which can improve classification performance of learning algorithm. Most of existing multi-source information fusion focus on the single decision system without considering multi-source and multi-label problems together. Nevertheless, data from different sources along with multiple labels simultaneously are absolutely frequent in many real-world applications. For this issue, in this paper, a multi-source multi-label decision system is proposed, which has more than one decision label. To remove some redundant or irrelevant features in multi-source multi-label decision system, a feature selection algorithm based on positive region for multi-source multi-label data is explored, which uses the feature dependency carried on the fusion decision table. Finally, examples are introduced to elaborate the detail process of the proposed algorithm, and experimental results show the effective performance of the proposed algorithm on multi-source and multi-label data.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (Nos. 61966016 and 61502213), the Natural Science Foundation of Jiangxi Province (No. 20192BAB207018), the Scientific Research Project of Education department of Jiangxi Province (No. GJJ180200).

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Authors and Affiliations

  1. School of Software, Jiangxi Agricultural University, Nanchang, 330045, China

    Wenbin Qian & Jun Yang

  2. School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, 330045, China

    Sudan Yu & Yinglong Wang

  3. Huazhong University of Science and Technology, Wuhan, 430074, China

    Jihao Zhang

Authors
  1. Wenbin Qian
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  2. Sudan Yu
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  3. Jun Yang
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  4. Yinglong Wang
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  5. Jihao Zhang
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Correspondence to Jun Yang.

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Qian, W., Yu, S., Yang, J. et al. Multi-label feature selection based on information entropy fusion in multi-source decision system. Evol. Intel. 13, 255–268 (2020). https://doi.org/10.1007/s12065-019-00349-9

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  • DOI: https://doi.org/10.1007/s12065-019-00349-9

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