A Spacecraft Electrical Characteristics Multi-Label Classification Method Based on Off-Line FCM Clustering and On-Line WPSVM.

Journal: PloS one
Published Date: Nov 6, 2015

Abstract

This paper proposes a novel multi-label classification method for resolving the spacecraft electrical characteristics problems which involve many unlabeled test data processing, high-dimensional features, long computing time and identification of slow rate. Firstly, both the fuzzy c-means (FCM) offline clustering and the principal component feature extraction algorithms are applied for the feature selection process. Secondly, the approximate weighted proximal support vector machine (WPSVM) online classification algorithms is used to reduce the feature dimension and further improve the rate of recognition for electrical characteristics spacecraft. Finally, the data capture contribution method by using thresholds is proposed to guarantee the validity and consistency of the data selection. The experimental results indicate that the method proposed can obtain better data features of the spacecraft electrical characteristics, improve the accuracy of identification and shorten the computing time effectively.

Authors

  • Ke Li
    School of Ideological and Political Education, Shanghai Maritime University, Shanghai, China.
  • Yi Liu
    Department of Interventional Therapy, Ningbo No. 2 Hospital, Ningbo, China.
  • Quanxin Wang
    Ergonomics and Environment Control Laboratory, Beihang University, Beijing, China.
  • Yalei Wu
    Ergonomics and Environment Control Laboratory, Beihang University, Beijing, China.
  • Shimin Song
    China Academy of Space Technology, Beijing, China.
  • Yi Sun
    Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine, CA, USA.
  • Tengchong Liu
    China Academy of Space Technology, Beijing, China.
  • Jun Wang
    Department of Speech, Language, and Hearing Sciences and the Department of Neurology, The University of Texas at Austin, Austin, TX 78712, USA.
  • Yang Li
    Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.
  • Shaoyi Du
    Institute of Artificial Intelligence and Robotics, Xian Jiaotong University, Xian Shanxi Province, China.

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