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2009
Springer

An Empirical Study of Category Skew on Feature Selection for Text Categorization

9 years 10 months ago
An Empirical Study of Category Skew on Feature Selection for Text Categorization
In this paper, we present an empirical comparison of the effects of category skew on six feature selection methods. The methods were evaluated on 36 datasets generated from the 20 Newsgroups, OHSUMED, and Reuters-21578 text corpora. The datasets were generated to possess particular category skew characteristics (i.e., the number of documents assigned to each category). Our objective was to determine the best performance of the six feature selection methods, as measured by F-measure and Precision, regardless of the number of features needed to produce the best performance. We found the highest F-measure values were obtained by bi-normal separation and information gain and the highest Precision values were obtained by categorical proportional difference and chi-squared.
Mondelle Simeon, Robert J. Hilderman
Added 25 May 2010
Updated 25 May 2010
Type Conference
Year 2009
Where AI
Authors Mondelle Simeon, Robert J. Hilderman
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