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Scoring and Selecting Terms for Text Categorization
May/June 2005 (vol. 20 no. 3)
pp. 40-47
Elena Monta?, University of Oviedo
Irene D?az, University of Oviedo
Jos? Ranilla, University of Oviedo
El?as F. Combarro, University of Oviedo
Javier Fern?ndez, University of Oviedo
Machine learning has become one of the main approaches to tackling text categorization. Because text domains present much irrelevant information, effective feature reduction is essential to improve classifiers' effectiveness and efficiency. A set of new scoring measures for feature selection taken from the machine learning domain were evaluated over two well-known collections of documents. Some of these measures outperformed traditional measures from information retrieval and information theory in certain situations.
Index Terms:
feature selection, text categorization, support vector machines, machine learning, information retrieval
Citation:
Elena Monta?, Irene D?az, Jos? Ranilla, El?as F. Combarro, Javier Fern?ndez, "Scoring and Selecting Terms for Text Categorization," IEEE Intelligent Systems, vol. 20, no. 3, pp. 40-47, May-June 2005, doi:10.1109/MIS.2005.49
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