Abstract
In text categorization, different supervised term weighting methods have been applied to improve classification performance by weighting terms with respect to different categories, for example, Information Gain, χ 2 statistic, and Odds Ratio. From the literature there are three term ranking methods to summarize term weights of different categories for multi-class text categorization. They are Summation, Average, and Maximum methods. In this paper we present a new term ranking method to summarize term weights, i.e. Maximum Gap. Using two different methods of information gain and χ 2 statistic, we setup controlled experiments for different term ranking methods. Reuter-21578 text corpus is used as the dataset. Two popular classification algorithms SVM and Boostexter are adopted to evaluate the performance of different term ranking methods. Experimental results show that the new term ranking method performs better.
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References
Lan, M., Tan, C.L., Low, H.-B.: Proposing a new term weighting scheme for text categorization. In: AAAI. AAAI Press, Menlo Park (2006)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing & Management 24(5), 513–523 (1988)
Debole, F., Sebastiani, F.: Supervised term weighting for automated text categorization. In: SAC, pp. 784–788. ACM, New York (2003)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Fisher, D.H. (ed.) ICML, pp. 412–420. Morgan Kaufmann, San Francisco (1997)
Duch, W., Duch, G.: Filter methods. In: Feature Extraction, Foundations and Applications, pp. 89–118. Physica Verlag, Springer (2004)
Liu, Y., Loh, H.T., Youcef-Toumi, K., Tor, S.B.: Handling of Imbalanced Data in Text Classification: Category-Based Term Weights. In: Kao, A., Poteet, S.R. (eds.) Natural Language Processing and Text Mining, p. 171 (2006)
Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
Lewis, D.D.: Reuters-21578 text categorization test collection. Distribution 1.3 (2004)
Hsu, C.W., Chang, C.C., Lin, C.J., et al.: A practical guide to support vector classification (2003)
Schapire, R.E., Singer, Y.: Boostexter: A boosting-based system for text categorization. Machine Learning 39(2/3), 135–168 (2000)
Joachims, T., Nedellec, C., Rouveirol, C.: Text categorization with support vector machines: learning with many relevant. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Li, T., Zhang, C., Zhu, S.: Empirical studies on multi-label classification. In: ICTAI, pp. 86–92. IEEE Computer Society, Los Alamitos (2006)
Salton, G.: Developments in automatic text retrieval. Science 253(5023), 974–980 (1991)
Mammadov, M.A., Rubinov, A.M., Yearwood, J.: The study of drug-reaction relationships using global optimization techniques. Optimization Methods and Software 22(1), 99–126 (2007)
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Mammadov, M., Yearwood, J., Zhao, L. (2010). A New Supervised Term Ranking Method for Text Categorization. In: Li, J. (eds) AI 2010: Advances in Artificial Intelligence. AI 2010. Lecture Notes in Computer Science(), vol 6464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17432-2_11
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DOI: https://doi.org/10.1007/978-3-642-17432-2_11
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