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Erschienen in:
2009 | OriginalPaper | Buchkapitel
An Empirical Study of Category Skew on Feature Selection for Text Categorization
verfasst von : Mondelle Simeon, Robert Hilderman
Erschienen in: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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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.