Abstract
Web directory hierarchy is critical to serve user’s search request. Creating and maintaining such directories without human experts involvement requires good classification of web documents. In this paper, we explore web page classification using keywords from documents as attributes and using the random forest learning methods. Our initially results are promising that the random forests learning method performed better than several other well known learning methods. When the number of topics increased from five to seven, random forests still performed better than other methods even though absolute classification rates decreased.
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Klassen, M., Paturi, N. (2010). Web Document Classification by Keywords Using Random Forests. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds) Networked Digital Technologies. NDT 2010. Communications in Computer and Information Science, vol 88. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14306-9_26
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DOI: https://doi.org/10.1007/978-3-642-14306-9_26
Publisher Name: Springer, Berlin, Heidelberg
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