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Comparative study of the Performance of Machine Learning Text Classifiers Applied to Afaan Oromo Text

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  • Etana Fikadu

Abstract

The aim of this study is to find the optimal method that can be used to classify Afaan Oromo text among different classifier by using the same number of text document. Automatic text classification has been needed in many fields for a long time. Many methods are used to classify text. The performance of this classifier we used in this study is measured in terms of recall, precision and F-measure. Finally we compare the efficiencies of the Bayesian Network, Naïve Bayesian, IBK and SMO to classify Afaan Oromo text. Experimental results on the same set of Afaan Oromo documents used before show that SMO slightly outperforms the other methods. Comparison reported in this paper shows that the SMO classifier exceeds the other four Machine learning classifier.

Suggested Citation

  • Etana Fikadu, 2020. "Comparative study of the Performance of Machine Learning Text Classifiers Applied to Afaan Oromo Text," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 6(4), pages 77-83, August.
    • Handle: RePEc:jbh:ijsrcs:v6:y2020:i4:id:hcseit20645
    DOI: 10.32628/CSEIT20645
    Note: Article URL: https://ijsrcseit.com/CSEIT20645
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