Feature selection by integrating document frequency with genetic algorithm for Amharic news document classification

Text classification is the process of categorizing documents based on their content into a predefined set of categories. Text classification algorithms typically represent documents as collections of words and it deals with a large number of features. The selection of appropriate features becomes important when the initial feature set is quite large. In this paper, we present a hybrid of document frequency (DF) and genetic algorithm (GA)-based feature selection method for Amharic text classification. We evaluate this feature selection method on Amharic news documents obtained from the Ethiopian News Agency (ENA). The number of categories used in this study is 13. Our experimental results showed that the proposed feature selection method outperformed other feature selection methods utilized for Amharic news document classification. Combining the proposed feature selection method with Extra Tree Classifier (ETC) improves classification accuracy. It improves classification accuracy up to 1% higher than the hybrid of DF, information gain (IG), chi-square (CHI), and principal component analysis (PCA), 2.47% greater than GA and 3.86% greater than a hybrid of DF, IG, and CHI.

Automated summary

Text classification categorizes documents based on their content into predefined categories. Selecting appropriate features is crucial when dealing with a large number of features. This paper presents a hybrid feature selection method combining document frequency and genetic algorithm for Amharic text classification, which outperforms other methods and improves classification accuracy when combined with Extra Tree Classifier.