doi: 10.4304/jsw.6.9.1837-1843
An Improved Algorithm of Bayesian Text Categorization
- 1 School of Computer, Communication University of China, Beijing, China
- 2 Dept. of Computer Science, Nipissing University, North Bay, Canada
Abstract
Text categorization is a fundamental methodology of text mining and a hot topic of the research of data mining and web mining in recent years. It plays an important role in building traditional information retrieval, web indexing architecture, Web information retrieval, and so on. This paper presents an improved algorithm of text categorization that combines the feature weighting technique with Naïve Bayesian classifier. Experimental results show that using the improved Gini index algorithm to feature weight can improve the performance of Naïve Bayesian classifier effectively. This algorithm obtains good application in the sensitive information recognition system.
Keywords
- text categorization
- Gini index
- feature
How to Cite
Tao Dong, Wenqian Shang, and Haibin Zhu, "An Improved Algorithm of Bayesian Text Categorization," Journal of Software, vol. 6, no. 9, pp. 1837-1843, 2011. https://doi.org/ 10.4304/jsw.6.9.1837-1843
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).