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Feature selection for text classification with Naive Bayes
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DOI：10.1016/j.eswa.2008.06.054.png)
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Abstract
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As an important preprocessing technology in text classification, feature selection can improve the scalability, efficiency and accuracy of a text classifier. In general, a good feature selection method should consider domain and algorithm characteristics. As the Naive Bayesian classifier is very simple and efficient and highly sensitive to feature selection, so the research of feature selection specially for it is significant. This paper presents two feature evaluation metrics for the Naive Bayesian classifier applied on multi-class text datasets: Multi-class Odds Ratio (MOR), and Class Discriminating Measure (CDM). Experiments of text classification with Naive Bayesian classifiers were carried out on two multi-class texts collections. As the results indicate, CDM and MOR gain obviously better selecting effect than other feature selection approaches. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords:
Text classification
Feature selection
Text preprocessing
Naive Bayes
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