A Novel Statistical Feature Selection Approach for Text Categorization


Mohamed Abdel Fattah, Journal of Information Processing Systems Vol. 13, No. 5, pp. 1397-1409, Oct. 2017  

https://doi.org/10.3745/JIPS.02.0076
Keywords: Electronic Texts, E-mail Filtering, Feature Selection, SMS Spam Filtering, Text Categorization
Fulltext:

Abstract

For text categorization task, distinctive text features selection is important due to feature space high dimensionality. It is important to decrease the feature space dimension to decrease processing time and increase accuracy. In the current study, for text categorization task, we introduce a novel statistical feature selection approach. This approach measures the term distribution in all collection documents, the term distribution in a certain category and the term distribution in a certain class relative to other classes. The proposed method results show its superiority over the traditional feature selection methods.


Statistics
Show / Hide Statistics

Statistics (Past 3 Years)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.




Cite this article
[APA Style]
Fattah, M. (2017). A Novel Statistical Feature Selection Approach for Text Categorization . Journal of Information Processing Systems, 13(5), 1397-1409. DOI: 10.3745/JIPS.02.0076.

[IEEE Style]
M. A. Fattah, "A Novel Statistical Feature Selection Approach for Text Categorization ," Journal of Information Processing Systems, vol. 13, no. 5, pp. 1397-1409, 2017. DOI: 10.3745/JIPS.02.0076.

[ACM Style]
Mohamed Abdel Fattah. 2017. A Novel Statistical Feature Selection Approach for Text Categorization . Journal of Information Processing Systems, 13, 5, (2017), 1397-1409. DOI: 10.3745/JIPS.02.0076.