Abstract
Detecting concept drift is important for dealing with real-world online learning problems. To detect concept drift in a small number of examples, methods that have an online classifier and monitor its prediction errors during the learning have been developed. We have developed such a detection method that uses a statistical test of equal proportions. Experimental results showed that our method performed well in detecting the concept drift in five synthetic datasets that contained various types of concept drift.
This study was partly supported by a Grant-in-Aid for JSPS Fellows (18-4475) from the Japan Society for the Promotion of Science.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23(1), 69–101 (1996)
Basseville, M., Nikiforov, I.V.: Detection of Abrupt Changes: Theory and Application. Prentice-Hall, Englewood Cliffs (1993)
Markou, M., Singh, S.: Novelty detection: a review — part 1: Statistical approaches. Signal Processing 83(12), 2481–2497 (2003)
Markou, M., Singh, S.: Novelty detection: a review — part 2: Neural network based approaches. Signal Processing 83(12), 2499–2521 (2003)
Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Proc. 17th Brazilian Symp. Artificial Intelligence, pp. 285–295 (2004)
Baena-García, M., del Campo-Ávila, J., Fidalgo, R., Bifet, A., Gavaldà, R., Morales-Bueno, R.: Early drift detection method. In: Proc. ECML/PKDD 2006, Work. Knowledge Discovery from Data Streams, pp. 77–86 (2006)
Nishida, K., Yamauchi, K., Omori, T.: ACE: Adaptive classifiers-ensemble system for concept-drifting environments. In: Proc. 6th Int. Work. Multiple Classifier Systems, pp. 176–185 (2005)
Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Proc. 9th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, pp. 226–235 (2003)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nishida, K., Yamauchi, K. (2007). Detecting Concept Drift Using Statistical Testing. In: Corruble, V., Takeda, M., Suzuki, E. (eds) Discovery Science. DS 2007. Lecture Notes in Computer Science(), vol 4755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75488-6_27
Download citation
DOI: https://doi.org/10.1007/978-3-540-75488-6_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75487-9
Online ISBN: 978-3-540-75488-6
eBook Packages: Computer ScienceComputer Science (R0)