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Research and Application of Fast Multi-label SVM Classification Algorithm Using Approximate Extreme Points

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Big Data Computing and Communications (BigCom 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9784))

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Abstract

In Large-Scale of Multi-label classification framework, applications of Non-linear kernel support vector machines (SVMs) classification algorithm are restricted by the problem of excessive training time. Hence, we propose Approximate Extreme Points Multi-label Support Vector Machine (AEMLSVM) classification algorithm to solve this problem. The first step of AEMLSVM classification algorithm is using approximate extreme points method to extract the training subsets, called the representative sets, from training dataset. Then SVM is trained from the representative sets. In addition, the AEMLSVM classification algorithm also can adopt Cost-Sensitive method to deal with the imbalanced data issue. Experiment results from three Large-Scale public datasets show that AEMLSVM classification algorithm can substantially shorten training time greatly and obtain a similar result compared with the traditional Multi-label SVM classification algorithm. It also exceeds existing fast Multi-label SVM classification algorithm in both training time and effectiveness. Besides, AEMLSVM classification algorithm has advantages in the classification time.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (NSFC) under the grant number 61170258, 61103196, 61379127, 61379128, 61572448 and by the Shandong Provincial Natural Science Foundation of China under the grant number ZR2014JL043.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Ocean University of China, Qingdao, China

    Zhongwei Sun, Zhongwen Guo, Xi Wang & Chao Liu

  2. Department of Computer Foundation, Ocean University of China, Qingdao, China

    Mingxing Jiang

Authors
  1. Zhongwei Sun
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  2. Zhongwen Guo
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  3. Mingxing Jiang
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  4. Xi Wang
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  5. Chao Liu
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Corresponding author

Correspondence to Zhongwen Guo .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of N. Carolina at Charlotte, Charlotte, North Carolina, USA

    Yu Wang

  2. Northeastern University, College of Information Science and Engineering, Shenyang, Liaoning, China

    Ge Yu

  3. Department of Electrical & Computer Engineering, Rutgers University, Piscataway, New Jersey, USA

    Yanyong Zhang

  4. Department of Electrical and Computer Engineering, University of Houston Department of Engineering, Houston, Texas, USA

    Zhu Han

  5. College of Information Science and Engineering, Northeastern University, Shenyang , Liaoning, China

    Guoren Wang

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Sun, Z., Guo, Z., Jiang, M., Wang, X., Liu, C. (2016). Research and Application of Fast Multi-label SVM Classification Algorithm Using Approximate Extreme Points. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds) Big Data Computing and Communications. BigCom 2016. Lecture Notes in Computer Science(), vol 9784. Springer, Cham. https://doi.org/10.1007/978-3-319-42553-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-42553-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42552-8

  • Online ISBN: 978-3-319-42553-5

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