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

Demeke Endalie¹, Getamesay Haile¹, Wondmagegn Taye Abebe²

¹Faculty of Computing and Informatics, Jimma Institute of Technology, Jimma, Oromia, Ethiopia.

Peerj. Computer Science

|May 31, 2022

Summary

This study introduces a hybrid feature selection method combining document frequency (DF) and genetic algorithms (GA) for Amharic text classification. The novel approach significantly enhances classification accuracy for Amharic news documents.

Keywords:

Chi-square Document frequency Extra tree classifier Feature selection Genetic algorithm Information gain Text classification

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Area of Science:

Natural Language Processing
Machine Learning
Information Retrieval

Background:

Text classification involves categorizing documents using word-based representations, often leading to large feature sets.
Effective feature selection is crucial for optimizing performance in text classification tasks.
Existing methods may not be optimal for morphologically rich languages like Amharic.

Discussion:

A hybrid feature selection method integrating document frequency (DF) and genetic algorithms (GA) is proposed for Amharic text classification.
The method was evaluated on 13 categories of Amharic news documents from the Ethiopian News Agency (ENA).
Performance was compared against other feature selection techniques, including combinations of DF, Information Gain (IG), Chi-Square (CHI), and Principal Component Analysis (PCA).

Key Insights:

The proposed DF-GA hybrid feature selection method demonstrates superior performance over existing techniques for Amharic text classification.
Combining the DF-GA method with the Extra Tree Classifier (ETC) further boosts classification accuracy.
The enhanced method achieved up to 1% higher accuracy than DF-IG-CHI-PCA, 2.47% higher than GA alone, and 3.86% higher than DF-IG-CHI.

Outlook:

Further research could explore the applicability of this hybrid method to other low-resource languages.
Investigating different classifier algorithms in conjunction with the proposed feature selection could yield additional improvements.
Expanding the dataset and category scope would provide a more comprehensive evaluation of the method's robustness.