Abstract:
The most important characteristic of text categorization is the high dimensionality even for the moderate size dataset. Feature selection, which can reduce the size of th...Show MoreMetadata
Abstract:
The most important characteristic of text categorization is the high dimensionality even for the moderate size dataset. Feature selection, which can reduce the size of the dimensionality without sacrificing the performance of the categorization and avoid over-fitting, is a commonly used approach in dimensionality reduction. In this paper, we proposed a new feature selection, which evaluates the deviation from the centroid based on both inter-category and intra-category. We compared the proposed method with four well-known feature selection algorithms using support vector machines on three benchmark datasets (20-newgroups, reuters-21578 and webkb). The experimental results show that the proposed method can significantly improve the performance of the classifier.
Date of Conference: 30 June 2014 - 02 July 2014
Date Added to IEEE Xplore: 01 September 2014
Electronic ISBN:978-1-4799-5604-3