OVERVIEW AND COMPARISON OF THREE CLASSIFIERS: ARABIC DOCUMENTS AS A CASE STUDY

Year 2017, , 274 - 277, 30.06.2017

Essam Hanandeh

https://doi.org/10.17261/Pressacademia.2017.600

Abstract

Nowadays,  text classification is
used in various fields of research and applications, such as information
retrieval, text mining, and data mining. This study tests the Naïve Bayes,
K-Nearest Neighbors, and Support Vector Machine algorithms on a relatively
large dataset of Arabic documents. This dataset comprise 1,000 Arabic documents
that are distributed across 10 classes. This comparison is based on recall and
precision measures. The evaluation results show that the Support Vector Machine
algorithms classifier outperforms the other two

Keywords

Arabic text categorization, KNN, NB, SVM, text mining

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