Abstract
Networked data are, nowadays, collected in various application domains such as social networks, biological networks, sensor networks, spatial networks, peer-to-peer networks etc. Recently, the application of data stream mining to networked data, in order to study their evolution over time, is receiving increasing attention in the research community. Following this main stream of research, we propose an algorithm for mining ranking models from networked data which may evolve over time. In order to properly deal with the concept drift problem, the algorithm exploits an ensemble learning approach which allows us to weight the importance of learned ranking models from past data when ranking new data. Learned models are able to take the network autocorrelation into account, that is, the statistical dependency between the values of the same attribute on related nodes. Empirical results prove the effectiveness of the proposed algorithm and show that it performs better than other approaches proposed in the literature.
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References
Aiolli, F.: A preference model for structured supervised learning tasks. In: ICDM, pp. 557–560. IEEE Computer Society (2005)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Statistics/Probability Series. Wadsworth Publishing Company, Belmont (1984)
Crammer, K., Singer, Y.: Pranking with ranking. In: NIPS, pp. 641–647. MIT Press (2001)
Dembczyski, K., Kotlowski, W., Slowiski, R., Szelag, M.: Learning of rule ensembles for multiple attribute ranking problems. In: Fürnkranz, J., Hüllermeier, E. (eds.) Preference Learning, pp. 217–247. Springer (2010)
Doyle, J.: Prospects for preferences. Computational Intelligence 20(2), 111–136 (2004)
Draper, N.R., Smith, H.: Applied regression analysis. Wiley series in probability and mathematical statistics. Wiley, New York (1996)
Draper, N.R., Smith, H.: Applied regression analysis. John Wiley & Sons (1982)
Har-Peled, S., Roth, D., Zimak, D.: Constraint Classification: A New Approach to Multiclass Classification. In: Cesa-Bianchi, N., Numao, M., Reischuk, R. (eds.) ALT 2002. LNCS (LNAI), vol. 2533, pp. 365–379. Springer, Heidelberg (2002)
Har-Peled, S., Roth, D., Zimak, D.: Constraint classification for multiclass classification and ranking. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems 15 (NIPS 2002), pp. 785–792 (2003)
Herbrich, R., Graepel, T., Bollmann-sdorra, P., Obermayer, K.: Learning preference relations for information retrieval (1998)
Herbrich, R., Graepel, T., Obermayer, K.: Large margin rank boundaries for ordinal regression. MIT Press (2000)
Jensen, D., Neville, J.: Linkage and autocorrelation cause feature selection bias in relational learning. In: Proc. 9th Intl. Conf. on Machine Learning, pp. 259–266. Morgan Kaufmann (2002)
Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 133–142. ACM, New York (2002)
Karalic, A.: Linear regression in regression tree leaves. In: Proceedings of ECAI 1992, pp. 440–441. John Wiley & Sons (1992)
Lubinsky, D.: Tree structured interpretable regression. In: Fisher, D., Lenz, H.J. (eds.) Learning from Data. Lecture Notes in Statistics. Springer (1994)
Macchia, L., Ceci, M., Malerba, D.: Mining Ranking Models from Dynamic Network Data. In: Perner, P. (ed.) MLDM 2012. LNCS, vol. 7376, pp. 566–577. Springer, Heidelberg (2012)
Malerba, D., Esposito, F., Ceci, M., Appice, A.: Top-down induction of model trees with regression and splitting nodes. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 612–625 (2004)
Neville, J., Simsek, O., Jensen, D.: Autocorrelation and relational learning: Challenges and opportunities. In: Wshp. Statistical Relational Learning (2004)
Newman, M.E.J., Watts, D.J.: The structure and dynamics of networks. Princeton University Press (2006)
Opitz, D., Maclin, R.: Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research 11, 169–198 (1999)
Robinson, W.S.: Ecological Correlations and the Behavior of Individuals. American Sociological Review 15(3), 351–357 (1950)
Stojanova, D., Ceci, M., Appice, A., Džeroski, S.: Network Regression with Predictive Clustering Trees. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 333–348. Springer, Heidelberg (2011)
Street, W.N., Kim, Y.: A streaming ensemble algorithm (sea) for large-scale classification. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2001, pp. 377–382. ACM, New York (2001)
Swanson, B.J.: Autocorrelated rates of change in animal populations and their relationship to precipitation. Conservation Biology 12(4), 801–808 (1998)
Tesauro, G.: Connectionist learning of expert preferences by comparison training. In: Advances in Neural Information Processing Systems 1, pp. 99–106. Morgan Kaufmann Publishers Inc., San Francisco (1989)
Torgo, L.: Functional models for regression tree leaves. In: Fisher, D.H. (ed.) ICML, pp. 385–393. Morgan Kaufmann (1997)
Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 226–235. ACM, New York (2003)
Wang, H., Yin, J., Pei, J., Yu, P.S., Yu, J.X.: Suppressing model overfitting in mining concept-drifting data streams. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2006, pp. 736–741. ACM, New York (2006)
Wang, Y., Witten, I.H.: Induction of model trees for predicting continuous classes. In: Poster papers of the 9th European Conference on Machine Learning. Springer (1997)
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Macchia, L., Ceci, M., Malerba, D. (2012). Learning to Rank from Concept-Drifting Network Data Streams. In: Liddle, S.W., Schewe, KD., Tjoa, A.M., Zhou, X. (eds) Database and Expert Systems Applications. DEXA 2012. Lecture Notes in Computer Science, vol 7446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32600-4_28
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DOI: https://doi.org/10.1007/978-3-642-32600-4_28
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