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Improvement of E-MIMLSVM+ Algorithm Based on Semi-Supervised Learning

In: Recent Developments in Data Science and Business Analytics

Author

Listed:
  • Wenqing Huang

    (School of Information, Zhejiang Sci-Tech University Hangzhou)

  • Hui You

    (School of Information, Zhejiang Sci-Tech University Hangzhou)

  • Li Mei

    (School of Information, Zhejiang Sci-Tech University Hangzhou)

  • Yinlong Chen

    (School of Information, Zhejiang Sci-Tech University Hangzhou)

  • Mingzhu Huang

    (School of Information, Zhejiang Sci-Tech University Hangzhou)

Abstract

The MIMLSVM algorithm is to transform the MIML learning problem into a single-instance multi-label learning problem, which is used as a bridge to degenerate into a single-instance single-label learning. However, this degradation algorithm is relatively easy to understand, but in the degradation process will lose some information, affecting the classification effect. By using multi-tasking learning, E-MIMLSVM+ is used to combine tag relevance to improve the algorithm MIMLSVM+. In order to make full use of the unlabeled samples to improve the classification accuracy, the paper improves MIMLSVM algorithm by using the semi-supervised learning method. Experimental results show that the proposed method can achieve higher classification accuracy.

Suggested Citation

  • Wenqing Huang & Hui You & Li Mei & Yinlong Chen & Mingzhu Huang, 2018. "Improvement of E-MIMLSVM+ Algorithm Based on Semi-Supervised Learning," Springer Proceedings in Business and Economics, in: Madjid Tavana & Srikanta Patnaik (ed.), Recent Developments in Data Science and Business Analytics, chapter 0, pages 417-423, Springer.
    • Handle: RePEc:spr:prbchp:978-3-319-72745-5_48
    DOI: 10.1007/978-3-319-72745-5_48
    as

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