Naive Bayes Classifier based Arabic document categorization
Hatem M. Noaman, Samir Elmougy, Ahmed Ghoneim, Taher Hamza · International Conference on Informatics and Systems · 2010
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 Naive 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 Naive Bayes (NB) Classifier gives ~62.23% average accuracy and decreases the dimensionality of the training documents.