An anti-noise text categorization method based on support vector machines

被引：0
|
作者：
Chen, L [1 ]
Huang, J [1 ]
Gong, ZH [1 ]
机构：
[1] Natl Univ Def Technol, Sch Comp Sci, Changsha 410073, Peoples R China
来源：
ADVANCES IN WEB INTELLIGENCE, PROCEEDINGS | 2005年 / 3528卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Text categorization has become one of the key techniques for handling and organizing web data. Though the native features of SVM (Support Vector Machines) axe better than Naive Bayes' for text categorization in theory, the classification precision of SVM is lower than Bayesian method in real world. This paper tries to find out the mysteries by analyzing the shortages of SVM, and presents an anti-noise SVM method. The improved method has two characteristics: 1) It chooses the optimal n-dimension classifying hyperspace. 2) It separates noise samples by preprocessing, and trains the classifier using noise free samples. Compared with naive Bayes method, the classification precision of anti-noise SVM is increased about 3 to 9 percent.
引用
收藏
页码：272 / 278
页数：7
相关论文
共 50 条
  • [1] Support Vector Machines based on a semantic kernel for text categorization
    Siolas, G
    d'Alché-Buc, F
    [J]. IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL V, 2000, : 205 - 209
  • [2] Feature selection for support vector machines in text categorization
    Liu, Y
    Lu, HM
    Lu, ZX
    Wang, P
    [J]. MLMTA'03: INTERNATIONAL CONFERENCE ON MACHINE LEARNING; MODELS, TECHNOLOGIES AND APPLICATIONS, 2003, : 129 - 134
  • [3] A wrapper approach with support vector machines for text categorization
    Montanés, E
    Quevedo, JR
    Díaz, I
    [J]. COMPUTATIONAL METHODS IN NEURAL MODELING, PT 1, 2003, 2686 : 230 - 237
  • [4] Virtual relevant documents in text categorization with support vector machines
    Lee, Kyung-Soon
    Kageura, Kyo
    [J]. INFORMATION PROCESSING & MANAGEMENT, 2007, 43 (04) : 902 - 913
  • [5] Least Squares Twin Support Vector Machines for Text Categorization
    Kumar, M. Arun
    Gopal, M.
    [J]. PROCEEDINGS OF THE 2015 39TH NATIONAL SYSTEMS CONFERENCE (NSC), 2015,
  • [6] Support vector machines for text categorization in Chinese question classification
    Lin, Xu-Dong
    Peng, Hong
    Liu, Bo
    [J]. 2006 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, (WI 2006 MAIN CONFERENCE PROCEEDINGS), 2006, : 334 - +
  • [7] A method of Chinese text categorization based on proximal support vector machine
    Zhou, JG
    Wang, K
    Wu, J
    Yan, PL
    Wu, M
    [J]. PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9, 2005, : 1615 - 1619
  • [8] Informative Vector Machines for text categorization
    Stankovic, Milos
    Stankovic, Srdan
    [J]. NEUREL 2006: EIGHT SEMINAR ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING, PROCEEDINGS, 2006, : 99 - +
  • [9] Improving performance of text categorization by combining filtering and support vector machines
    Díaz, I
    Ranilla, J
    Montañes, E
    Fernández, J
    Combarro, EF
    [J]. JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, 2004, 55 (07): : 579 - 592
  • [10] Experiments on kernel tree support vector PF machines for text categorization
    Methasate, Ithipan
    Theeramunkong, Thanaruk
    [J]. ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, 2007, 4426 : 720 - +
12345