Efficient Text Classifier Using Rough Sets and Hybrid Classifier Approach: A Case Study in Elfagr Newspaper
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Open Access | Fully Refereed | Peer Reviewed International Journal

ISSN: 2319-7064



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Research Paper | Computer Science & Engineering | Egypt | Volume 6 Issue 7, July 2017

Efficient Text Classifier Using Rough Sets and Hybrid Classifier Approach: A Case Study in Elfagr Newspaper

Mohamed Omran, O.E. Emam, Laila Abd-Elatif, M. Thabet

Text classification is the process of classifying documents into predefined categories based on their content. It is the automated assignment of natural language texts to predefined categories. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data. Existing supervised learning algorithms for classifying text need sufficient documents to learn accurately. This paper presents an algorithm based on rough set for the automatic grouping of PDF documents, and with potential application for Web document classification.

Keywords: rough sets, classifier, elfagr newspaper

Edition: Volume 6 Issue 7, July 2017

Pages: 2169 - 2173

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How to Cite this Article?

Mohamed Omran, O.E. Emam, Laila Abd-Elatif, M. Thabet, "Efficient Text Classifier Using Rough Sets and Hybrid Classifier Approach: A Case Study in Elfagr Newspaper", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=ART20175719, Volume 6 Issue 7, July 2017, 2169 - 2173

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