By Topic

Naive Bayes Classifier based Arabic document categorization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Noaman, H.M. ; Fac. of Comput. & Inf. Sci., Mansoura Univ., Mansoura, Egypt ; Elmougy, S. ; Ghoneim, A. ; Hamza, T.

Text Categorization aims to assign an electronic document to one or more categories based on its contents. Due to the rapid growth of the number of online Arabic documents, the information libraries and Arabic document corpus, automatic Arabic document classification becomes an important task. This paper suggests the use of rooting algorithm with Nai¿ve Bayes Classifier to the problem of document categorization of Arabic language and reports the algorithm performance in terms of error rate, accuracy, and micro-average recall measures. Our experimental study shows that using rooting algorithm with Nai¿ve Bayes (NB) Classifier gives ~62.23% average accuracy and decreases the dimensionality of the training documents.

Published in:

Informatics and Systems (INFOS), 2010 The 7th International Conference on

Date of Conference:

28-30 March 2010