Resource title

Boosting Classifiers for Drifting Concepts

Resource image

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Resource description

This paper proposes a boosting-like method to train a classifier ensemble from data streams. It naturally adapts to concept drift and allows to quantify the drift in terms of its base learners. The algorithm is empirically shown to outperform learning algorithms that ignore concept drift. It performs no worse than advanced adaptive time window and example selection strategies that store all the data and are thus not suited for mining massive streams.

Resource author

Martin Scholz, Ralf Klinkenberg

Resource publisher

Resource publish date

Resource language

eng

Resource content type

text/html

Resource resource URL

http://hdl.handle.net/10419/22652

Resource license

Adapt according to the presented license agreement and reference the original author.