Text Classification for Arabic Words Using BPSO/REP-Tree
Hamza Naji, Wesam Ashour, Mohammed Al Hanjouri
International Journal of Computational Linguistics Research, Vol. 9, No. 1, 2018.
DOI: 
Link: http://www.dline.info/jcl/fulltext/v9n1/jclv9n1_1.pdf
Abstract:
Text Classification is the process of grouping documents into categories based on their contents. Many Text Classification (TC) algorithms have been proposed for Arabic TC. The main idea in text Arabic classification is the accuracy of the results, which these results depends on the correctness of the text classification phases which start
with the prepossessing phase and end with feature selection and the choosing of the best classifier that can classify text in related groups. In this paper we provide a new system for text classification based on BPSO/REP-Tree hybrid. The first term refers to the â€œBinary Particle Swarm Optimizationâ€ that we use it for the feature selection process and the second term refers the classifier we used â€œReduced Error Pruning Treeâ€. We will show the results of the experiments on a data-set collected from the BBC-Arabic website using the Weka tool which specific for data classification.