New Drift Detection Method for Data Streams
Correctly detecting the position where a concept begins to drift is important in mining data streams. In this paper, we propose a new method for detecting concept drift. The proposed method, which can detect different types of drift, is based on processing data chunk by chunk and measuring differences between two consecutive batches, as drift indicator. In order to evaluate the proposed method we measure its performance on a set of artificial datasets with different levels of severity and speed of drift. The experimental results show that the proposed method is capable to detect drifts and can approximately find concept drift locations.
KeywordsConcept drift stream mining drift detection evolving data
Unable to display preview. Download preview PDF.
- 2.Kuncheva, L.I.: Classifier ensembles for detecting concept change in streaming data: Overview and perspectives. In: Proc. of the Second Workshop SUEMA, Patras, Greece, pp. 5–9 (2008)Google Scholar
- 4.Baena-Garcia, M., Del Campo-Avila, J., Fidalgo, R., Bifet, A.: Early drift detection method. In: Proc. of the 4th ECML PKDD International Workshop on Knowledge Discovery from Data Streams, Berlin, Germany, pp. 77–86 (2006)Google Scholar
- 7.Minku, F.L., White, A., Yao, X.: The impact of diversity on on-line ensemble learning in the presence of concept drift. In: IEEE TKDE, vol. 22, pp. 730–742 (2010)Google Scholar
- 8.Salganicoff, M.: Density-adaptive learning and forgetting. In: Proc. of the 10th Int. Conf. on Machine Learning, pp. 276–283 (1993)Google Scholar
- 9.Klinkenberg, R., Joachims, T.: Detecting concept drift with support vector machines. In: Proc. of the 17th Int. Conf. on Machine Learning, pp. 487–494 (2000)Google Scholar
- 10.Dries, A., Ruckert, U.: Adaptive concept drift detection. In: SDM, pp. 233–244. SIAM, Philadelphia (2009)Google Scholar
- 11.Gama, J.: Knowledge discovery from data streams. Data Mining and Knowledge Discovery Series. USA (2010)Google Scholar