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
         
        
        
        
        
        
            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;
         
        
        
        
            Conference_Titel : 
Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on
         
        
            Conference_Location : 
Funchal
         
        
        
            Print_ISBN : 
978-1-4244-7657-2
         
        
        
            DOI : 
10.1109/ITSC.2010.5624991