A study on rough set-aided feature selection for automatic Web-page classification

Web Intelligence and Agent Systems

Volumn 4, Issue 4, 2006, Pages 431-441

  Wakaki, Toshiko ^a   Itakura, Hiroyuki ^a   Tamura, Masaki ^b   Motoda, Hiroshi ^c   Washio, Takashi ^c  

^a SHIBAURA INSTITUTE OF TECHNOLOGY   (Japan)

^b JAPAN ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

^c OSAKA UNIVERSITY   (Japan)

Author keywords

C4.5; Dimensionality reduction; Feature selection; Rough set; Support Vector Machines; Web page classification

Indexed keywords

INFORMATION SERVICES; LEARNING SYSTEMS; ROUGH SET THEORY; SEARCH ENGINES; WEBSITES;

C4.5; DIMENSIONALITY REDUCTION; FEATURE SELECTION; SUPPORT VECTOR MACHINES; WEB PAGE CLASSIFICATION;

FEATURE EXTRACTION;

EID: 33845757650     PISSN: 15701263     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (15)

1. A. An, Y. Huang, X. Huang and N. Cercone, Feature Selection with Rough Sets for Web Page Classification, Trans Rough Sets (2004), 1-13.
2. J. Brank, M. Grobelnik, N. Milic-Frayling and D. Mladenic, Feature selection using support vector machines, Proceedings of the Third International Conference on Data Minig Methods and Databases for Engineering, Finance, and Other Fields, 2002, 25-27.
3. A. Chouchoulas and Q. Shen, Rough set-aided keyword reduction for text categorization, Applied Artificial Intelligence 15 (2001), 843-873.
4. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, 2000.
5. M. Dash and H. Liu, Consistency-based search in feature selection, Artificial Intelligence 151 (2003), 155-176.
6. J. Katzberg and W. Ziarko, Variable precision extension of rough sets, W. Ziarko, ed., Fundamenta Informaticae, Special Issue on Rough Sets 27(2-3) (1996), 155-168.
7. P. Lingras and C. Butz, Interval Set Classifiers using Support Vector Machines, Proceedings of the 2004 Conference of the North American Fuzzy Information Processing Society, Vol. 2, 2004, 707-710.
8. Z. Pawlak, Rough sets: Theoretical Aspect of Reasoning About Data, Kluwer Academic Publishers, 1991.
9. J.R. Quinlan, C4.5: Programs for Machine Learning, San Mateo, CA: Morgan Kaufmann, 1991.
10. Q. Shen and A. Chouchoulas, Rough set-based dimensionality reduction for supervised and unsupervised learning, International Journal of Applied Mathematics and Computer Science 11(3) (2001), 583-601.
11. K. Shima, Identifying Discriminative Features form High-Dimensional Data using Support Vector Machines, Ph. D. Thesis, University of Tokyo, Tokyo, Japan, 2003.
12. T. Tsukada, M. Washio and H. Matoda, Automatic web-page classification by using machine learning methods, Proceedings of the First Asia-Pacific Conference on Web Intelligence (WI 2001), LNAI 2198:303-313, 2001.
13. T. Wakaki, H. Itakura and M. tamura, Rough Set-Aided Feature Selection for Automatic Web-Page Classification, Proceedings of 2004 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2004), IEEE Computer Society, 2004, 70-76.
14. chasen. http://chasen.aist-nara.ac.jp/.
15. TinySVM. http://cl.aist-nara.ac.jp/~taku-ku/software/tinysvm /.