Abstract
Adaptive online learning algorithms have been successfully applied to fast-evolving data streams. Such streams are susceptible to concept drift, which implies that the most suitable type of classifier often changes over time. In this setting, a system that is able to seamlessly select the type of learner that presents the current “best” model holds much value. For example, in a scenario such as user profiling for security applications, model adaptation is of the utmost importance. We have implemented a multi-strategy framework, the so-called Tornado environment, which is able to run multiple and diverse classifiers simultaneously for decision making. In our framework, the current learner with the highest performance, at a specific point in time, is selected and the corresponding model is then provided to the user. In our implementation, we employ an Error-Memory-Runtime (EMR) measure which combines the error-rate, the memory usage and the runtime of classifiers as a performance indicator. We conducted experiments on synthetic and real-world datasets with the Hoeffding Tree, Naive Bayes, Perceptron, K-Nearest Neighbours and Decision Stumps algorithms. Our results indicate that our environment is able to adapt to changes and to continuously select the best current type of classifier, as the data evolve.
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Gama, J., Zliobaite, I., Bifet, A., Pecheniziky, M., Bouchachia, A.: A survey on concept drift adaptation. J. ACM Comput. Surv. 46(4), 1–37 (2014)
Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2001)
Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286–295. Springer, Heidelberg (2004). doi:10.1007/978-3-540-28645-5_29
Gama, J., Fernandes, R., Rocha, R.: Decision trees for mining data streams. J. Intell. Data Anal. 10(1), 23–45 (2006)
Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: SIAM International Conference on Data Mining, pp. 443–448 (2007)
Huang, D.T.J., Koh, Y.S., Dobbie, G., Bifet, A.: Drift detection using stream volatility. In: Appice, A., Rodrigues, P.P., Santos Costa, V., Soares, C., Gama, J., Jorge, A. (eds.) ECML PKDD 2015. LNCS (LNAI), vol. 9284, pp. 417–432. Springer, Heidelberg (2015). doi:10.1007/978-3-319-23528-8_26
Koren, Y.: Collaborative filtering with temporal dynamics. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 447–456 (2009)
Lee, W., Stolfo, S.J., Mok, K.W.: Adaptive intrusion detection: A data mining approach. J. Artif. Intell. Rev. 14(6), 533–567 (2000)
Stavens, D., Hoffmann, G., Thrun, S.: Online speed adaptation using supervised learning for high-speed, off-road autonomous driving. In: 20th International Joint Conference on Artificial Intelligence, pp. 2218–2224 (2007)
Bifet, A., Holmes, G., Pfahringer, B., Kirkby, R., Gavalda, R.: New ensemble methods for evolving data streams. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 139–148 (2009)
Bifet, A., Holmes, G., Pfahringer, B., Frank, E.: Fast perceptron decision tree learning from evolving data streams. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS (LNAI), vol. 6119, pp. 299–310. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13672-6_30
Zliobaite, I., Budka, M., Stahl, F.: Towards cost-sensitive adaptation: when is it worth updating your predictive model? Neurocomputing 150, 240–249 (2015)
Olorunnimbe, M.K., Viktor, H.L., Paquet, E.: Intelligent adaptive ensembles for data stream mining: A high return on investment approach. In: Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z.W. (eds.) NFMCP 2015. LNCS (LNAI), vol. 9607, pp. 61–75. Springer, Heidelberg (2016). doi:10.1007/978-3-319-39315-5_5
Gaber, M., Stahl, F., Gomes, J.B.: Pocket Data Mining: Big Data on Small Devices. Studies in Big Data. Springer, Heidelberg (2014)
Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: Massive online analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: An update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Kubat, M., Widmer, G.: Adapting to drift in continous domain. In: 8th European Conference on Machine Learning, pp. 307–310. Springer, Heidelberg (1995)
Gama, J., Sebastiao, R., Rodrigues, P.P.: On evaluating stream learning algorithms. J. Mach. Learn. 90(3), 317–346 (2013)
Domingos, P., Hulten, G.: Mining high-speed data streams. In: 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80 (2000)
Lichman., M.: UCI Machine Learning Repository. University of California Irvine, School of Information and Computer Science (2013)
Zupan, B., Bohanec, M., Bratko, I., Demsar, J.: Machine learning by function decomposition. In: International Conference on Machine Learning (ICML), pp. 421–429 (1997)
Harries, M.: Splice-2 Comparative Evaluation: Electricity Pricing. Technical Report, University of New South Wales, Australia (1999)
Cattral, R., Oppacher, F., Deugo, D.: Evolutionary data mining with automatic rule generalization. In: Recent Advances in Computers, Computing and Communications pp. 296–300 (2002)
Kohavi, P.: Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid. In: 2nd International Conference on Knowledge Discovery and Data Mining (1996)
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Pesaranghader, A., Viktor, H.L., Paquet, E. (2016). A Framework for Classification in Data Streams Using Multi-strategy Learning. In: Calders, T., Ceci, M., Malerba, D. (eds) Discovery Science. DS 2016. Lecture Notes in Computer Science(), vol 9956. Springer, Cham. https://doi.org/10.1007/978-3-319-46307-0_22
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DOI: https://doi.org/10.1007/978-3-319-46307-0_22
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