Linguistics

Philosophy

Support vector machine

Square (algebra)

Precision and recall

Arabic

Selection (genetic algorithm)

Classifier (UML)

Data mining

Computer science

Natural language processing

Pattern recognition (psychology)

Mathematics

Feature vector

Feature selection

Feature (linguistics)

Artificial intelligence

Geometry

Machine learning

Speech recognition

Journal of King Saud University - Computer and Information Sciences

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 improved method for Arabic text classification that employs the Chi-square feature selection (referred to, hereafter, as ImpCHI) to enhance the classification performance. Besides, we have also compared this improved chi-square with three traditional features selection metrics namely mutual information, information gain and Chi-square. Building on our previous work, we extend the current work to assess the method in terms of other evaluation methods using SVM classifier. For this purpose, a dataset of 5070 Arabic documents are classified into six independently classes. In terms of performance, the experimental findings show that combining ImpCHI method and SVM classifier outperforms other combinations in terms of precision, recall and f-measures. This combination significantly improves the performance of Arabic text classification model. The best f-measures obtained for this model is 90.50%, when the number of features is 900.

Feature selection using an improved Chi-square for Arabic text classification