A Comparison of Text Classification Methods k-NN, Naïve Bayes, and Support Vector Machine for News Classification
| Autor: | Yohan Muliono, Fidelson Tanzil, Fanny Fanny |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
lcsh:T58.5-58.64
business.industry Computer science lcsh:Information technology Machine learning computer.software_genre k-nearest neighbors algorithm Support vector machine Correlation Naive Bayes classifier Categorization Classification methods Preprocessor The Internet Artificial intelligence business computer |
| Zdroj: | Jurnal Informatika: Jurnal Pengembangan IT, Vol 3, Iss 2, Pp 157-160 (2018) |
| ISSN: | 2548-9356 2477-5126 |
| Popis: | In this era, a rapid thriving Internet occasionally complicates users to retrieve news category furthermore if there are plentiful of news to be categorized. News categorization is a technique can be used to retrieve a category of news which gives easiness for users. Internet has vast amounts of information especially at news. Therefore, accurate and speedy access is becoming ever more difficult. This paper compares a news categorization using k-Nearest Neighbor, Naive Bayes and Support Vector Machine. Using vary of variables and through a several steps of preprocessing which proving k-Nearest Neighbor is producing a capable accuracy competes with Support Vector Machine whereas Naive Bayes producing just an average result, not as good as k-Nearest Neighbor and Support Vector Machine yet as bad as k-Nearest Neighbor and Support Vector Machine ever reach. As the results, k-Nearest Neighbor using correlation measurement type produces the best result of this experiment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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