| Autor: |
Pravalika, T., Narasimha Prasad, L. V., Avula, Madhuri |
| Předmět: |
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| Zdroj: |
International Journal of Simulation: Systems, Science & Technology; 2021, Vol. 22 Issue 1, p1.1-1.6, 6p |
| Abstrakt: |
Text categorization is the assignment of natural language text to one or more predefined categories based on their content is an important component in many information organization and management task. We compare the effectiveness of four different automatic learning algorithms for text categorization in terms of learning speed and classification accuracy. We also examine training set size and alternative document representation very accurate text classifiers can be learned automatically from training example. Generally we have familiar with KNN is a best classification algorithm but the algorithm spends most time on the classification. In this paper we propose content order on different algorithms. The K- Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Random Tree Forest (RTF) grouping techniques are the strategies which are utilized in the implementation of this paper. Content order has an expanding significance since it permits programmed content association, and we compare the results SVM classification is a straightforward as well as exceptionally exact technique to classify the content and it take less time on the classification. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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