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DOI: 10.14569/IJACSA.2020.0110224
PDF

On Exhaustive Evaluation of Eager Machine Learning Algorithms for Classification of Hindi Verses

Author 1: Prafulla B. Bafna
Author 2: Jatinderkumar R. Saini

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 2, 2020.

  • Abstract and Keywords
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Abstract: Implementing supervised machine learning on the Hindi corpus for classification and prediction of verses is an untouched and useful area. Classifying and predictions benefits many applications like organizing a large corpus, information retrieval and so on. The metalinguistic facility provided by websites makes Hindi as a major language in the digital domain of information technology today. Text classification algorithms along with Natural Language Processing (NLP) facilitates fast, cost-effective, and scalable solution. Performance evaluation of these predictors is a challenging task. To reduce manual efforts and time spent for reading the document, classification of text data is important. In this paper, 697 Hindi poems are classified based on four topics using four eager machine-learning algorithms. In the absence of any other technique, which achieves prediction on Hindi corpus, misclassification error is used and compared to prove the betterment of the technique. Support vector machine performs best amongst all.

Keywords: Classification; eager machine learning algorithm; Hindi; prediction

Prafulla B. Bafna and Jatinderkumar R. Saini, “On Exhaustive Evaluation of Eager Machine Learning Algorithms for Classification of Hindi Verses” International Journal of Advanced Computer Science and Applications(IJACSA), 11(2), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110224

@article{Bafna2020,
title = {On Exhaustive Evaluation of Eager Machine Learning Algorithms for Classification of Hindi Verses},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110224},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110224},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {2},
author = {Prafulla B. Bafna and Jatinderkumar R. Saini}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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