A SVM Text Classification Approch Based on Binary Tree
Spport Vector Machine(SVM ) is based on minimal structure analysis principle, it can it can solve the dimension disaster, regionally minimal problems, etc. but the common SVM can only solve binary classification. Some research develope algorithm that can solve muti-class classification through constructing binary tree with several binary SVM, the research yeilds some fruits. Linguistics research result show that of all the extracted feature word, noun and verb make up a great proportions, about 65.5％. Based the above knowledge, we improve the SVM muti-class classification by introducing an algorithm of constructing binary tree, which use the Chinese part-of-speech information to reduce the dimension; we also optimize the binary tree node sequence by calculating the distances of the classes. Experimental results shows that the proposed SVMmulti-class classification have high precision and recall rate.
Support Vector Machine1 Text Classification 2 Binary Tree3 Part-of-Speech4
Zheng Weifa
Educational Technology Center, Guangdong University of Business Studies(GDBC), Guangzhou 510320, China
国际会议
重庆
英文
1418-1421
2009-12-25（万方平台首次上网日期，不代表论文的发表时间）