Abstract
Traditional researches on data stream mining only put emphasis on building classifiers with high accuracy, which always results in classifiers with dramatic drop of accuracy when concept drifts. In this paper, we present our RTRC system that has good classification accuracy when concept drifts and enough samples are scanned in data stream. By using Markov chain and least-square method, the system is able to predict not only on which the next concept is but also on when the concept is to drift. Experimental results confirm the advantages of our system over Weighted Bagging and CVFDT, two representative systems in streaming data mining.
This work is supported by NSF 60373108.
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Wang, Y., Li, Z., Zhang, Y., Zhang, L., Jiang, Y. (2006). Improving the Performance of Data Stream Classifiers by Mining Recurring Contexts. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_119
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DOI: https://doi.org/10.1007/11811305_119
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