Abstract
The Internet makes it possible to share and manipulate a vast quantity of information efficiently and effectively, but the rapid and chaotic growth experienced by the Net has generated a poorly organized environment that hinders the sharing and mining of useful data. The need for meaningful web-page classification techniques is therefore becoming an urgent issue. This paper describes a novel approach to web-page classification based on a fuzzy representation of web pages. A doublet representation that associates a weight with each of the most representative words of the web document so as to characterize its relevance in the document. This weight is derived by taking advantage of the characteristics of HTML language. Then a fuzzy-rule-based classifier is generated from a supervised learning process that uses a genetic algorithm to search for the minimum fuzzy-rule set that best covers the training examples. The proposed system has been demonstrated with two significantly different classes of web pages.
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Ribeiro, A., Fresno, V., Garcia-Alegre, M.C., Guinea, D. (2003). Web Page Classification: A Soft Computing Approach. In: Menasalvas, E., Segovia, J., Szczepaniak, P.S. (eds) Advances in Web Intelligence. AWIC 2003. Lecture Notes in Computer Science, vol 2663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44831-4_12
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DOI: https://doi.org/10.1007/3-540-44831-4_12
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