Abstract
Web page classification is an important research direction of web mining. In the paper, a SVM method of web page classification is presented. It include four steps: (1) using analysis module to extract the core text and structural tags from a web page; (2) adopting the improved VSM model to generate the initial feature vectors based on the core text of web page; (3) adjusting weights of the selected features based on structural tags in web page to generate the base SVM classifier; (4) combining the base classifiers produced by iteration based on Boosting mechanism to obtain the target SVM classifier. The experiment of web page classification shows that the approach presented is efficient.
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References
Yang, Y.: An evaluation of statistical approach to text categorization. Technical Report CMU-CS-97-127, Computer Science Department, Carnegie Mellon University (1997)
Joachims, T.: A Probabilistic Analysis of the Rocchio Algorithm with TF-IDF for Text Categorization. In: Proc. of ICML 1997, pp. 143–151. Morgan Kaufmann Publishers, San Francisco (1997)
Yu-chang, L., Ming-yu, L., Fan, L., Li-zhu, Z.: Analysis and construction of word weighing function in VSM. Computer Research and Development 39(10), 1205–1210 (2002)
Xianjun, X., Jiantao, S., Yuchang, L.: The research and implementation of a new result-faced methods for webpage information extraction. Computer Engineering and Application 38, 87–91 (2002)
Kecman, V.: Learning and Soft Computing, Support Verctor Machines. In: Neural Networks and Fuzzy Logic Models, The MIT Press, Cambridge (2001)
Wang, L.P. (ed.): Support Vector Machines: Theory and Application. Springer, Heidelberg (2005)
Jiantao, S., Dou, S., Yuchang, L.: Web document classification techniques. Journal of Tsinghua University 44(1), 65–68 (2004)
Diao, L., Lu, M., Hu, K., Lu, Y., Shi, C.: New boosting algorithms for text categorization. In: Proceedings of the World Congress on Intelligent Control and Automation (WCICA), vol. 3, pp. 2326–2329 (2002)
Lili, D., Yuchang, L., Chunyi, S.: A method to boost support vector machines. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 463–468. Springer, Heidelberg (2002)
Mingyu, L., Qiang, Z., Fan, L., et al.: Recommendation of Web Pages Based on Concept Association. In: Proc. of 4th IEEE International Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems, pp. 221–227. IEEE Computer Society Press, Los Alamitos (2002)
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Lu, M., Guo, C., Sun, J., Lu, Y. (2005). A SVM Method for Web Page Categorization Based on Weight Adjustment and Boosting Mechanism. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_100
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DOI: https://doi.org/10.1007/11540007_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
Online ISBN: 978-3-540-31828-6
eBook Packages: Computer ScienceComputer Science (R0)