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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Detecting Concept Drift Using Statistical Testing
verfasst von : Kyosuke Nishida, Koichiro Yamauchi
Erschienen in: Discovery Science
Verlag: Springer Berlin Heidelberg
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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.