Abstract
In this paper we propose a new methodology for link-based document classification based on probabilistic classifiers and Bayesian networks. We also report the results obtained of its application to the XML Document Mining Track of INEX’09.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Buntine, W.L.: A guide to the literature on learning probabilistic networks from data. IEEE Transactions on Knowledge and Data Engineering 8, 195–210 (1996)
Cano, A., Moral, S., Salmerón, A.: Algorithms for approximate probability propagation in Bayesian networks. In: Advances in Bayesian Networks, Studies in Fuzziness and Soft Computing, vol. 146, pp. 77–99. Springer, Heidelberg (2004)
de Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Romero, A.E.: OR gate Bayesian networks for text classification: a discriminative alternative approach to multinomial naive Bayes. In: XIV Congreso Español sobre Tecnologías y Lógica Fuzzy, pp. 385–390 (2008)
de Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Romero, A.E.: Probabilistic methods for structured document classification at INEX’07. In: Fuhr, N., Kamps, J., Lalmas, M., Trotman, A. (eds.) INEX 2007. LNCS, vol. 4862, pp. 195–206. Springer, Heidelberg (2008)
de Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Romero, A.E.: Probabilistic methods for link-based classification at INEX’08. In: Geva, S., Kamps, J., Trotman, A. (eds.) INEX 2008. LNCS, vol. 5631, pp. 453–459. Springer, Heidelberg (2009)
Denoyer, L., Gallinari, P.: Overview of the INEX 2008 XML Mining Track. In: Geva, S., Kamps, J., Trotman, A. (eds.) INEX 2008. LNCS, vol. 5631, pp. 401–411. Springer, Heidelberg (2009)
Elvira Consortium: Elvira: An environment for probabilistic graphical models. In: First European Workshop on Probabilistic Graphical Models, pp. 222–230 (2002)
Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning 20, 197–243 (1995)
McCallum, A., Nigam, K.: A Comparison of event models for Naive Bayes text classification. In: AAAI/ICML Workshop on Learning for Text Categorization, pp. 137–142. AAAI Press, Menlo Park (1998)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco (1988)
Platt, J.: Probabilistic outputs for Support Vector Machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers, pp. 61–74. MIT Press, Cambridge (1999)
Sebastiani, F.: Machine Learning in automated text categorization. ACM Computing Surveys 34, 1–47 (2002)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Yang, Y.: A study of thresholding strategies for text categorization. In: 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 137–145 (2001)
Yang, Y., Slattery, S.: A study of approaches to hypertext categorization. Journal of Intelligent Information Systems 18, 219–241 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Masegosa, A.R., Romero, A.E. (2010). Link-Based Text Classification Using Bayesian Networks. In: Geva, S., Kamps, J., Trotman, A. (eds) Focused Retrieval and Evaluation. INEX 2009. Lecture Notes in Computer Science, vol 6203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14556-8_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-14556-8_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14555-1
Online ISBN: 978-3-642-14556-8
eBook Packages: Computer ScienceComputer Science (R0)