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Target Recognition Method Based on Multi-class SVM and Evidence Theory

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Advances in Internetworking, Data & Web Technologies (EIDWT 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 6))

Abstract

In order to conquer the hard outputs defect of Support Vector Machine (SVM) and extend its application, an improved target recognition method based on Multi-class Support Vector Machine (MSVM) is proposed. Firstly, the typical Probability Modeling methodologies of MSVM were deeply analyzed. Secondly, the structure of one-against-one multi-class method which matches with Basic Probability Assignment (BPA) outputs of evidence theory by coincide, so a special Multi-class BPA output method is derived, and multi-sensor target recognition model based on MSVM and two-layer evidence theory is constructed. Finally, the results of experiments show that the proposed approach can not only conquer the overlap area of one-against-one multi-class method, but also improve classification accuracy.

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Acknowledgement

This work is supported by National Natural Science Foundation of China, Grant Nos 61273275, 60975026, 61503407.

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Authors and Affiliations

  1. Air Traffic Control and Navigation College, Air Force Engineering University, Xi’an, 710051, China

    Wen Quan & Maolin Gao

  2. Air and Missile Defense College, Air Force Engineering University, Xi’an, 710051, China

    Jian Wang & Lei Lei

Authors
  1. Wen Quan
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  2. Jian Wang
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  3. Lei Lei
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  4. Maolin Gao
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Corresponding author

Correspondence to Wen Quan .

Editor information

Editors and Affiliations

  1. Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. School of Computer Sciences, Hubei University of Technology, Wuhan, China

    Mingwu Zhang

  3. Department of Electronic Technology, Key, Engineering University of CAPF, Xi’an, Xizang, China

    Xu An Wang

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Quan, W., Wang, J., Lei, L., Gao, M. (2018). Target Recognition Method Based on Multi-class SVM and Evidence Theory. In: Barolli, L., Zhang, M., Wang, X. (eds) Advances in Internetworking, Data & Web Technologies. EIDWT 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-59463-7_26

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  • DOI: https://doi.org/10.1007/978-3-319-59463-7_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59462-0

  • Online ISBN: 978-3-319-59463-7

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