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Classifying Web Pages by Aimed Nation Using Machine Learning

Classifying Web Pages by Aimed Nation Using Machine Learning

Boudheb Tarik, Djelloul Daouadji Mahmoud, Elberrichi Zakaria
Copyright: © 2017 |Volume: 7 |Issue: 1 |Pages: 16
ISSN: 1947-9344|EISSN: 1947-9352|EISBN13: 9781522513254|DOI: 10.4018/IJOCI.2017010102
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MLA

Tarik, Boudheb, et al. "Classifying Web Pages by Aimed Nation Using Machine Learning." IJOCI vol.7, no.1 2017: pp.20-35. http://doi.org/10.4018/IJOCI.2017010102

APA

Tarik, B., Mahmoud, D. D., & Zakaria, E. (2017). Classifying Web Pages by Aimed Nation Using Machine Learning. International Journal of Organizational and Collective Intelligence (IJOCI), 7(1), 20-35. http://doi.org/10.4018/IJOCI.2017010102

Chicago

Tarik, Boudheb, Djelloul Daouadji Mahmoud, and Elberrichi Zakaria. "Classifying Web Pages by Aimed Nation Using Machine Learning," International Journal of Organizational and Collective Intelligence (IJOCI) 7, no.1: 20-35. http://doi.org/10.4018/IJOCI.2017010102

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Abstract

Classifying web pages is to automatically assign predefined class to them. It is one of the main applications of web mining. The authors' aim is to detect the targeted nation based on the web pages content. It is an original application. In this paper, the authors propose different web mining approaches using machine learning algorithms such as Naïve Bayes and Support Vector Machine in order classify them. They present detailed stages of the procedure. The best experimental result based on an original corpus created by their own means shows a very attention grabbing f-score of 85%.

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