In text mining, feature selection (FS) is a common method for reducing the huge number of
the space features and improving the accuracy of classification. In this paper, we propose an …

Text classification could be defined as the way of allocating text into predefined groups
according to its contents. Over the past few years, an increase emerged in the volume of …

Due to the large number of documents available in the internet, emails and digital libraries,
document classification is becoming a crucial task extremely required. It is commonly …

We propose a novel text classification model, which aims to improve the performance of
Arabic text classification using machine learning techniques. One of the effective solutions in …

This paper reports a comparative study of two machine learning methods on Arabic text
categorization. Based on a collection of news articles as a training set, and another set of …

A vast amount of valuable human knowledge is recorded in documents. The rapid growth in
the number of machine-readable documents for public or private access necessitates the …

Text classification is a supervised learning technique that uses labeled training data to
derive a classification system (classifier) and then automatically classifies unlabelled text …

feature selection (FS) is essential for effective and more accurate text classification (TC)
systems. This paper investigates the effectiveness of five commonly used FS methods for our …

Text classification is a supervised technique that uses labelled training data to learn the
classification system and then automatically classifies the remaining text using the learned …

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 …