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Enhanced Filter Feature Selection Methods for Arabic Text Categorization

Enhanced Filter Feature Selection Methods for Arabic Text Categorization

Abdullah Saeed Ghareb, Azuraliza Abu Bakara, Qasem A. Al-Radaideh, Abdul Razak Hamdan
Copyright: © 2018 |Volume: 8 |Issue: 2 |Pages: 24
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781522545644|DOI: 10.4018/IJIRR.2018040101
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MLA

Ghareb, Abdullah Saeed, et al. "Enhanced Filter Feature Selection Methods for Arabic Text Categorization." IJIRR vol.8, no.2 2018: pp.1-24. http://doi.org/10.4018/IJIRR.2018040101

APA

Ghareb, A. S., Abu Bakara, A., Al-Radaideh, Q. A., & Hamdan, A. R. (2018). Enhanced Filter Feature Selection Methods for Arabic Text Categorization. International Journal of Information Retrieval Research (IJIRR), 8(2), 1-24. http://doi.org/10.4018/IJIRR.2018040101

Chicago

Ghareb, Abdullah Saeed, et al. "Enhanced Filter Feature Selection Methods for Arabic Text Categorization," International Journal of Information Retrieval Research (IJIRR) 8, no.2: 1-24. http://doi.org/10.4018/IJIRR.2018040101

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Abstract

The filtering of a large amount of data is an important process in data mining tasks, particularly for the categorization of unstructured high dimensional data. Therefore, a feature selection process is desired to reduce the space of high dimensional data into small relevant subset dimensions that represent the best features for text categorization. In this article, three enhanced filter feature selection methods, Category Relevant Feature Measure, Modified Category Discriminated Measure, and Odd Ratio2, are proposed. These methods combine the relevant information about features in both the inter- and intra-category. The effectiveness of the proposed methods with Naïve Bayes and associative classification is evaluated by traditional measures of text categorization, namely, macro-averaging of precision, recall, and F-measure. Experiments are conducted on three Arabic text datasets used for text categorization. The experimental results showed that the proposed methods are able to achieve better and comparable results when compared to 12 well known traditional methods.

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