Abstract
The success of simple methods for classification shows that is is often not necessary to model complex attribute interactions to obtain good classification accuracy on practical problems. In this article, we propose to exploit this phenomenon in the data stream context by building an ensemble of Hoeffding trees that are each limited to a small subset of attributes. In this way, each tree is restricted to model interactions between attributes in its corresponding subset. Because it is not known a priori which attribute subsets are relevant for prediction, we build exhaustive ensembles that consider all possible attribute subsets of a given size. As the resulting Hoeffding trees are not all equally important, we weigh them in a suitable manner to obtain accurate classifications. This is done by combining the log-odds of their probability estimates using sigmoid perceptrons, with one perceptron per class. We propose a mechanism for setting the perceptrons’ learning rate using the change detection method for data streams, and also use to reset ensemble members (i.e., Hoeffding trees) when they no longer perform well. Our experiments show that the resulting ensemble classifier outperforms bagging for data streams in terms of accuracy when both are used in conjunction with adaptive naive Bayes Hoeffding trees, at the expense of runtime and memory consumption. We also show that our stacking method can improve the performance of a bagged ensemble.
- Bifet, A. and Gavaldà, R. 2007. Learning from time-changing data with adaptive windowing. In Proceedings of the SIAM International Conference on Data Mining.Google Scholar
- Bifet, A., Holmes, G., Pfahringer, B., and Gavaldà, R. 2009a. Improving adaptive bagging methods for evolving data streams. In Proceedings of the 1st Asian Conference on Machine Learning (ACML). 23--37. Google ScholarDigital Library
- Bifet, A., Holmes, G., Pfahringer, B., Kirkby, R., and Gavaldà, R. 2009b. New ensemble methods for evolving data streams. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 139--148. Google ScholarDigital Library
- Bifet, A., Holmes, G., Kirkby, R., and Pfahringer, B. 2010a. MOA: Massive online analysis. J. Mach. Learn. Res. 11, 1601--1604. Google ScholarDigital Library
- Bifet, A., Holmes, G., and Pfahringer, B. 2010b. Leveraging bagging for evolving data streams. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD). Part I. 135--150. Google ScholarDigital Library
- Bifet, A., Holmes, G., Pfahringer, B., and Frank, E. 2010c. Fast perceptron decision tree learning from evolving data streams. In Proceedings of the 14th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD). Part II. 299--310. Google ScholarDigital Library
- Breiman, L. 2001. Random forests. In Machine Learning, 5--32. Google ScholarDigital Library
- Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. 1984. Classification and Regression Trees. Wadsworth.Google Scholar
- Domingos, P. and Hulten, G. 2000. Mining high-speed data streams. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 71--80. Google ScholarDigital Library
- Frank, A. and Asuncion, A. 2010. UCI machine learning repository. http://archive.ics.uci.edu/ml/citation_policy.html.Google Scholar
- Gama, J., Rocha, R., and Medas, P. 2003. Accurate decision trees for mining high-speed data streams. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 523--528. Google ScholarDigital Library
- Gama, J., Medas, P., Castillo, G., and Rodrigues, P. P. 2004. Learning with drift detection. In Proceedings of the SBIA Brazilian Symposium on Artificial Intelligence. 286--295.Google Scholar
- Gama, J., Sebastião, R., and Rodrigues, P. P. 2009. Issues in evaluation of stream learning algorithms. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 329--338. Google ScholarDigital Library
- Harries, M. 1999. Splice-2 comparative evaluation: Electricity pricing. Tech. rep., The University of South Wales.Google Scholar
- Ho, T. K. 1998. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20, 8, 832--844. Google ScholarDigital Library
- Holmes, G., Kirkby, R., and Pfahringer, B. 2005. Stress-Testing Hoeffding trees. In Proceedings of the 9th European Conference on Knowledge Discovery in Databases (PKDD’05). 495--502. Google ScholarDigital Library
- Hulten, G., Spencer, L., and Domingos, P. 2001. Mining time-changing data streams. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 97--106. Google ScholarDigital Library
- Kirkby, R. 2007. Improving hoeffding trees. Ph.D. thesis, University of Waikato.Google Scholar
- Kolter, J. Z. and Maloof, M. A. 2007. Dynamic weighted majority: An ensemble method for drifting concepts. J. Mach. Learn. Res. 8, 2755--2790. Google ScholarDigital Library
- Margineantu, D. D. and Dietterich, T. G. 1997. Pruning adaptive boosting. In Proceedings of the 14th International Conference on Machine Learning (ICML). 211--218. Google ScholarDigital Library
- Oza, N. C. and Russell, S. J. 2001a. Experimental comparisons of online and batch versions of bagging and boosting. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 359--364. Google ScholarDigital Library
- Oza, N. C. and Russell, S. J. 2001b. Online bagging and boosting. In Proceedings of the Conference on Artificial Intelligence and Statistics. 105--112.Google Scholar
- Wolpert, D. H. 1992. Stacked generalization. Neural Netw. 5, 2, 241--259. Google ScholarDigital Library
Index Terms
- Ensembles of Restricted Hoeffding Trees
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