Abstract
Training of compound ensemble classifier systems might be computationally complex and hence time consuming task. Not only elementary classifiers are to be trained, but also model of the ensemble has to be updated. Therefore, an efficiency of the training shall be considered as a compound quality which consists of not only a classification accuracy but also a running time. This gains a special importance while dealing with data streams where data arrive at high pace and the system update shall be done promptly. In this paper we present an application of Simulated Annealing based algorithm for training of data stream processing ensemble. The evaluation of our method is performed in series of experiments which show that our ensemble perform very effectively in term of accuracy and processing time.
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References
Alpaydin, E.: Introduction to Machine Learning (Adaptive Computation and Machine Learning), vol. 5. The MIT Press (2004). http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/0262012111
Bishop, C.M.: Pattern Recognition and Machine Learning, Information Science and Statistics, vol. 4. Springer (2006). http://www.library.wisc.edu/selectedtocs/bg0137.pdf
Chen, S., Wang, H., Zhou, S., Yu, P.S.: Stop chasing trends: discovering high order models in evolving data. In: 2008 IEEE 24th International Conference on Data Engineering, pp. 923–932 (2008), http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4497501
Duin, R.P.W., Juszczak, P., Paclik, P., Pekalska, E., de Ridder, D., Tax, D.: PRTools4, A Matlab Toolbox for Pattern Recognition. Delft University of Technology (2004)
Eiben, A.E., Smith, J.: Introduction to Evolutionary Computing. Springer (2003)
Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: SBIA Brazilian Symposium on Artificial Intelligence, pp. 286–295. Springer (2004)
Jackowski, K.: Fixed-size ensemble classifier system evolutionarily adapted to a recurring context with an unlimited pool of classifiers. Pattern Analysis and Applications, February 2013. http://link.springer.com/10.1007/s10044-013-0318-x
Kuncheva, L.I.: Classifier Ensembles for Changing Environments, pp. 1–15 (2004)
van Laarhoven, P.J.M., Aarts, E.H.L.: Introduction. In: Simulated Annealing: Theory and Applications, pp. 1–6. Springer, Netherlands (1987). http://link.springer.com/10.1007/978-94-015-7744-1_1
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 (2001). http://portal.acm.org/citation.cfm?doid=502512.502568
Tsymbal, A.: The problem of concept drift: definitions and related work (2004)
Widmer, G., Kubat, M.: Effective learning in dynamic environments by explicit context tracking. In: European Conference on Machine Learning, pp. 227–243. Springer (1993)
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Jackowski, K. (2018). A Novel Simulated Annealing Based Training Algorithm for Data Stream Processing Ensemble Classifier. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_46
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DOI: https://doi.org/10.1007/978-3-319-59162-9_46
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