Abstract
Relational networks often evolve over time by the addition, deletion, and changing of links, nodes, and attributes. However, accurately incorporating the full range of temporal dependencies into relational learning algorithms remains a challenge. We propose a novel framework for discovering temporal-relational representations for classification. The framework considers transformations over all the evolving relational components (attributes, edges, and nodes) in order to accurately incorporate temporal dependencies into relational models. Additionally, we propose temporal ensemble methods and demonstrate their effectiveness against traditional and relational ensembles on two real-world datasets. In all cases, the proposed temporal-relational models outperform competing models that ignore temporal information.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bifet, A., Holmes, G., Pfahringer, B., Kirkby, R., Gavaldà, R.: New ensemble methods for evolving data streams. In: SIGKDD, pp. 139–148 (2009)
Chakrabarti, S., Dom, B., Indyk, P.: Enhanced hypertext categorization using hyperlinks. In: SIGMOD, pp. 307–318 (1998)
Cortes, C., Pregibon, D., Volinsky, C.: Communities of Interest. In: Hoffmann, F., Adams, N., Fisher, D., Guimarães, G., Hand, D.J. (eds.) IDA 2001. LNCS, vol. 2189, pp. 105–114. Springer, Heidelberg (2001)
Dietterich, T.G.: Ensemble Methods in Machine Learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
Domingos, P., Pazzani, M.: On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning 29, 103–130 (1997)
Domingos, P., Richardson, M.: Mining the network value of customers. In: SIGKDD, pp. 57–66 (2001)
Dunlavy, D., Kolda, T., Acar, E.: Temporal link prediction using matrix and tensor factorizations. TKDD 5(2), 10 (2011)
Eldardiry, H., Neville, J.: Across-model collective ensemble classification. AAAI (2011)
Güneş, İ., Çataltepe, Z., Öğüdücü, Ş.G.: GA-TVRC: A Novel Relational Time Varying Classifier to Extract Temporal Information Using Genetic Algorithms. In: Perner, P. (ed.) MLDM 2011. LNCS, vol. 6871, pp. 568–583. Springer, Heidelberg (2011)
Lahiri, M., Berger-Wolf, T.: Structure prediction in temporal networks using frequent subgraphs. In: CIDM, pp. 35–42 (2007)
McGovern, A., Collier, N., Matthew Gagne, I., Brown, D., Rodger, A.: Spatiotemporal Relational Probability Trees: An Introduction. In: ICDM, pp. 935–940 (2008)
Neville, J., Jensen, D., Friedland, L., Hay, M.: Learning relational probability trees. In: SIGKDD, pp. 625–630 (2003)
Neville, J., Jensen, D., Gallagher, B.: Simple estimators for relational Bayesian classifers. In: ICML, pp. 609–612 (2003)
Preisach, C., Schmidt-Thieme, L.: Relational ensemble classification. In: ICDM, pp. 499–509. IEEE (2006)
Preisach, C., Schmidt-Thieme, L.: Ensembles of relational classifiers. KIS 14(3), 249–272 (2008)
Rossi, R., Neville, J.: Modeling the evolution of discussion topics and communication to improve relational classification. In: SOMA-KDD, pp. 89–97 (2010)
Rossi, R.A., Neville, J.: Representations and ensemble methods for dynamic relational classification. CoRR abs/1111.5312 (2011)
Sharan, U., Neville, J.: Temporal-relational classifiers for prediction in evolving domains. In: ICML (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rossi, R., Neville, J. (2012). Time-Evolving Relational Classification and Ensemble Methods. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30217-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-30217-6_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30216-9
Online ISBN: 978-3-642-30217-6
eBook Packages: Computer ScienceComputer Science (R0)