Abstract
In order to exploit the dependencies in relational data to improve predictions, relational classification models often need to make simultaneous statistical judgments about the class labels for a set of related objects. Robustness has always been an important concern for such collective classification models since many real-world relational data such as Web pages are often accompanied with much noisy information. In this paper, we propose a contextual dependency network (CDN) model for classifying linked objects in the presence of noisy and irrelevant links. The CDN model makes use of a dependency function to characterize the contextual dependencies among linked objects so that it can effectively reduce the effect of irrelevant links on the classification. We show how to use the Gibbs inference framework over the CDN model for collective classification of multiple linked objects. The experiments show that the CDN model demonstrates relatively high robustness on datasets containing irrelevant links.
This work is supported by China-America Digital Academic Library project (grant No. CADAL2004002).
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References
Jensen, D., Neville, J., Gallagher, B.: Why collective inference improves relational classification. In: Proc. 10th ACM Int’l Conf. on Knowledge Discovery and Data Mining, pp. 593–598 (2004)
Taskar, B., Abbeel, P., Koller, D.: Discriminative Probabilistic Models for Relational Classification. In: Proc. of Uncertainty on Artificial Intelligence, Edmonton, Canada, pp. 485–492 (2002)
Yang, Y., Slattery, S., Ghani, R.: A Study of Approaches to Hypertext Categorization. J. Intelligent Information system 2/3, 219–241 (2002)
Chakrabarti, S., Dom, B., Indyk, P.: Enhanced Hypertext Categorization Using Hyperlinks. In: Proc. of SIGMOD 1998, pp. 307–318 (1998)
Neville, J., Jensen, D., Friedland, L., Hay, M.: Learning relational probability trees. In: Proc. 9th ACM Int’l Conf. on Knowledge Discovery and Data Mining, pp. 625–630 (2003)
Lu, Q., Getoor, L.: Link-based Classification. In: Proc. 12th Int’l Conf. on Machine Learning, pp. 496–503 (2003)
Friedman, N., Koller, D., Taskar, B.: Learning Probabilistic Models of Relational Structure. J. Machine Learning Research, 679–707 (2002)
Neville, J., Jensen, D.: Collective Classification with Relational Dependency Networks. In: Proc. 2nd Multi-Relational Data Mining Workshop in KDD-2003 (2003)
Neville, J., Jensen, D.: Dependency Networks for Relational Data. In: Proc. IEEE Int’l Conf. on Data Mining, pp. 170–177 (2004)
Richardson, M., Domingos, P.: Markov Logic Networks. Machine Learning 26(1-2), 107–136 (2005)
Heckerman, D., Chickering, D., Meek, C., Rounthwaite, R., Kadie, C.: Dependency Networks for Inference, Collaborative Filtering, and Data Visualization. J. Machine Learning Research 1, 49–75 (2001)
Sollich, P.: Probabilistic methods for Support Vector Machines. In: Proc. Advances in Neural Information Processing Systems, vol. 12, pp. 349–355. MIT Press, Cambridge (2000)
Zhong, S., Ghosh, J.: A New Formulation of Coupled Hidden Markov Models. Tech. Report, Dept. of Electronic and Computer Engineering, U. of Texas at Austin, USA (2001)
Tian, Y.H., Huang, T.J., Gao, W.: Latent linkage semantic kernels for collective classification of link data. J. Intelligent Information Systems (in press, 2006)
Heckerman, D., Meek, C., Koller, D.: Probabilistic Models for Relational Data, Tech. Report, MSR-TR-2004-30, Microsoft Research (2004)
Uwents, W., Blockeel, H.: Classifying relational data with neural networks. In: Proc. 15th Int’l Conf. on Inductive Logic Programming, Bonn, Germany, pp. 384–396 (2005)
Neville, J., Jensen, D., Gallagher, B.: Simple estimators for relational Bayesian classifiers. In: Proc. 3rd IEEE Int’l Conf. on Data Mining, pp. 609–612 (2003)
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Tian, Y., Huang, T., Gao, W. (2006). Robust Collective Classification with Contextual Dependency Network Models. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_19
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DOI: https://doi.org/10.1007/11811305_19
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