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Experimental evaluation of feature selection methods for text classification

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1 Author(s)
Uchyigit, G. ; Sch. of Comput., Eng. & Math., Univ. of Brighton, Lewes, UK

In this paper we present the experiments of a comparative study of feature selection methods used for text classification. Ten feature selection methods were evaluated in this study, including a new feature selection method, called the GU metric. The other feature selection methods evaluated in this study are: Chi-Squared (χ2) statistic, NGL coefficient, GSS coefficient, Mutual Information, Information Gain, Odds Ratio, Term Frequency, Fisher Criterion, BSS/WSS coefficient. The experimental evaluations show that the GU metric obtained the best F1 and F2 scores. The experiments were performed on the 20 Newsgroups data sets with the Naive Probabilistic Classifier.

Published in:

Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on

Date of Conference:

29-31 May 2012