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Top1. Introduction
The volume of text in its digital form is continuously increasing, and it has become an area of research interest to investigate and construct new techniques and methods that can handle and control this considerable amount of text. Text categorization techniques have become one of the key technologies for text pattern recognition and text organization; however, these techniques struggle with the large dimensionality of text because there is a huge number of text features. Therefore, the feature selection (FS) process can be of benefit to categorization techniques, and it is used as a categorization pre-process for dimensionality reduction and relevant feature selection that can simplify the categorization process and identify precisely the pattern of text features (Mesleh, 2011; Uguz, 2011).
Text categorization is a forecasting process of text document categories that categorizes text documents into one/multiple pre-defined categories based on the extracted knowledge from text documents (Manning and Schutze, 1999). In recent years, many categorization methods were proposed for categorizing text of different languages such as: K-nearest neighbour (KNN) (Abu Tair & Baraka, 2013; Jiang et al., 2012), support vector machine (SVM) (Joachims, 1998; Mesleh, 2011), naïve Bayes (NB) (Chen et al., 2009; Hattab & Hussein, 2013) decision tree (Harrag et al., 2010), and associative classification (AC) (Al-Radaideh et al., 2011; Chaing et al., 2008; Ghareb et al., 2012; Srvidhya & Anitha, 2009; Khorsheed & Al-Thubaity, 2013).
The high dimensionality of the feature space is a major problem in Arabic text categorization due to the existence of noisy and irrelevant features, which adversely affect the categorization performance and degrades computer resource (Abu Tair & Baraka, 2013; Sharef et al., 2012; Zahran & Kanaan, 2009). Therefore, the feature selection process is frequently used to reduce the high dimensionality of text and select the most informative features for Arabic text categorization. It can be defined as “a process that chooses an optimal subset of features according to certain criterion” (Liu & Motoda, 1998). Feature selection techniques can be categorized into groups based on their functionality for feature evaluation. As cited in the literature, feature selection methods for Arabic text categorization can be divided into filtering feature selection and meta-heuristic/wrapper approaches. The filter feature selection methods are frequently used for Arabic text categorization because of their efficiency and simplicity. They include chi-square, odd ratio, document frequency, information gain, and many others (Mesleh, 2011; Harrag et al., 2010).