Active Learning Classifier for Streaming Data
This work reports the research on active learning approach applied to the data stream classification. The chosen characteristics of the proposed frameworks were evaluated on the basis of the wide range of computer experiments carried out on the three benchmark data streams. Obtained results confirmed the usability of proposed method to the data stream classification with the presence of incremental concept drift.
KeywordsPattern classification Data stream classification Active learning
This work was supported by the Polish National Science Centre under the grant no. DEC-2013/09/B/ST6/02264. This work was supported by EC under FP7, Coordination and Support Action, Grant Agreement Number 316097, ENGINE European Research Centre of Network Intelligence for Innovation Enhancement (http://engine.pwr.wroc.pl/). All computer experiments were carried out using computer equipment sponsored by ENGINE project.
- 1.Bifet, A., Gavalda R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the Seventh SIAM International Conference on Data Mining. SIAM, Minneapolis, Minnesota, USA, 26–28 April 2007Google Scholar
- 2.Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: Moa: massive online analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)Google Scholar
- 3.Bifet, A., Holmes, G., Pfahringer, B., Kirkby, R., Gavaldà, R.: New ensemble methods for evolving data streams. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 139–148. ACM, New York (2009)Google Scholar
- 6.Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (2013, in press)Google Scholar
- 10.Kurlej, B., Wozniak, M.: Impact of window size in active learning of evolving data streams. In: Proceedings of the 45th International Conference on Modelling and Simulation of Systems, MOSIS 2011, pp. 56–62 (2011)Google Scholar
- 11.Lazarescu, M.M., Venkatesh, S., Bui, H.H.: Using multiple windows to track concept drift. Intell. Data Anal. 8(1), 29–59 (2004)Google Scholar
- 13.Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23(1), 69–101 (1996)Google Scholar