A Comparative Study of Centroid-Based and Naïve Bayes Classifiers for Document Categorization
Journal Title: International Journal of engineering Research and Applications - Year 2017, Vol 7, Issue 3
Abstract
Assigning documents to related categories is critical task which is used for effective document retrieval. Automatic text classification is the process of assigning new text document to the predefined categories based on its content. In this paper, we implemented and performed comparison of Naïve Bayes and Centroid-based algorithms for effective document categorization of English language text. In Centroid Based algorithm, we used Arithmetical Average Centroid (AAC) and Cumuli Geometric Centroid (CGC) methods to calculate centroid of each class. Experiment is performed on R-52 dataset of Reuters-21578 corpus. Micro Average F1 measure is used to evaluate the performance of classifiers. Experimental results show that Micro Average F1 value for NB is greatest among all followed by Micro Average F1 value of CGC which is greater than Micro Average F1 of AAC. All these results are valuable for future research.
Authors and Affiliations
Rupali P. Patil, R. P. Bhavsar, B. V. Pawar
Keywords
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- EP ID EP390838
- DOI 10.9790/9622- 0703045963.
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