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Self-Universum support vector machine

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Abstract

In this paper, for an improved twin support vector machine (TWSVM), we give it a theoretical explanation based on the concept of Universum and then name it Self-Universum support vector machine (SUSVM). For the binary classification problem, SUSVM takes the positive class and negative class as Universum separately to construct two classification problems with Universum; therefore, two nonparallel hyperplanes are derived. SUSVM has several improved advantages compared with TWSVMs. Furthermore, we improve SUSVM by formulating it as a pair of linear programming problems instead of quadratic programming problems (QPPs), which leads to the better generalization performance and less computational time. The effectiveness of the enhanced method is demonstrated by experimental results on several benchmark datasets.

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Acknowledgments

This work has been partially supported by grants from National Natural Science Foundation of China (Nos. 11271361, 71331005), Major International (Ragional) Joint Research Project (No. 71110107026), the Ministry of water resources’ special funds for scientific research on public causes (No. 201301094).

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Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China

    Dalian Liu

  2. Department of Basic Course Teaching, Beijing Union University, Beijing, 100101, China

    Dalian Liu

  3. Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, 100190, China

    Yingjie Tian & Yong Shi

  4. College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China

    Rongfang Bie

Authors
  1. Dalian Liu
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  2. Yingjie Tian
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  3. Rongfang Bie
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  4. Yong Shi
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Corresponding authors

Correspondence to Yingjie Tian or Yong Shi.

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Liu, D., Tian, Y., Bie, R. et al. Self-Universum support vector machine. Pers Ubiquit Comput 18, 1813–1819 (2014). https://doi.org/10.1007/s00779-014-0797-9

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  • DOI: https://doi.org/10.1007/s00779-014-0797-9

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