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Feature selection method using improved CHI Square on Arabic text classifiers: analysis and application
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DOI：10.1007/s11042-020-10074-6.png)
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Abstract
En 中文
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 information in the varied fields on the Internet, thus making the classification of texts one of the most important, yet challenging. Text classification is commonly employed in numerous applications and for different objectives. The extensive and broad use of the Internet, particularly in the Arab world, as well as the massive number of the documents and pages which are provided in the Arabic language, raised the need for having suitable tools for classification of these pages and documents by their main categories. The aim of this paper to study the effect of the improved CHI (ImpCHI) Square on the performance of six well-known classifiers: Random Forest, Decision Tree, Naive Bayes, Naive Bayes Multinomial, Bayes Net, and Artificial Neural Networks. These proposed techniques are quite important for improving classification of Arabic documents and can be regarded as a promising basis for the stage of text classification because it contributes to the classification of the texts into predefined categories. This combination method takes the advantages of more than one technique, which can produce better results in the final outcomes. The dataset employed in this paper includes 9055 Arabic documents that were collected from various Arabic resources. Based on their content, these documents were divided into twelve categories. Four performance evaluation criteria were used: the F-measure, recall, precision, and Time build model. The experimental results show that the use of ImpCHI square gives better classification results than the normal CHI square method with all studied classifiers, in terms of all used performance criteria.
Keywords:
Text classification algorithms
Bayes net
Naï ve Bayes
Random Forest
Decision tree
Artificial neural networks
CHI Square
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