Feature sub-set selection metrics for Arabic text classification
Pattern Recognition LettersPublished 8 August 2011
Abdelwadood Mesleh
Citations65
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TL;DR
An empirical comparison of seventeen traditional FSS metrics for TC tasks reveals that Chi-square and Fallout F SS metrics work best for Arabic TC tasks.
Abstract
Feature sub-set selection (FSS) is an important step for effective text classification (TC) systems. This paper presents an empirical comparison of seventeen traditional FSS metrics for TC tasks. The TC is restricted to support vector machine (SVM) classifier and only for Arabic articles. Evaluation used a corpus that consists of 7842 documents independently classified into ten categories. The experimental results are presented in terms of macro-averaging precision, macro-averaging recall and macro-averaging F1 measures. Results reveal that Chi-square and Fallout FSS metrics work best for Arabic TC tasks.
Keywords
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