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.
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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
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DOI: https://doi.org/10.1007/978-3-540-75488-6_27
Publisher Name: Springer, Berlin, Heidelberg
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