• DocumentCode
    2899794
  • Title

    Study on information fusion algorithm and application based on improved SVM

  • Author

    Wang, Yanhui ; Zhang, Chenchen ; Luo, Jun

  • Author_Institution
    State Key Lab. of Rail Traffic Control & Safety, Beijing Jiaotong Univ., Beijing, China
  • fYear
    2010
  • fDate
    19-22 Sept. 2010
  • Firstpage
    1271
  • Lastpage
    1276
  • Abstract
    Authors presented the information fusion algorithm based on improved SVM, namely, decision tree - support vector machine algorithm (Decision Tree Method-Support Vector Mechines, DTM-SVM). The algorithm overcame the limitations of the conventional SVM classification which applied only to two-classification problem by a “one to many” pattern, solved multi-classification problem and met a wider range of application requirements. Finally, based on the establishment of a freeway traffic state identification evaluation system, the DTM-SVM model was applied to solve the freeway traffic state recognition. Results show that: the algorithm can identify in a shorter time to reach higher recognition accuracy.
  • Keywords
    decision trees; pattern classification; road traffic; sensor fusion; support vector machines; traffic engineering computing; decision tree method; freeway traffic state recognition; information fusion algorithm; multiclassification problem; support vector machines; Algorithm design and analysis; Classification algorithms; Indexes; Kernel; Support vector machines; Traffic control; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on
  • Conference_Location
    Funchal
  • ISSN
    2153-0009
  • Print_ISBN
    978-1-4244-7657-2
  • Type

    conf

  • DOI
    10.1109/ITSC.2010.5624991
  • Filename
    5624991