Authors: | Klinkenberg, Ralf Scholz, Martin |
Title: | Boosting classifiers for drifting concepts |
Language (ISO): | en |
Abstract: | 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. |
Subject Headings: | Base learners Boosting-like method Classifier ensemble Data stream Drift Mining massive streams |
URI: | http://hdl.handle.net/2003/22236 http://dx.doi.org/10.17877/DE290R-14320 |
Issue Date: | 2006-03-16T13:29:57Z |
Appears in Collections: | Sonderforschungsbereich (SFB) 475 |
Files in This Item:
File | Description | Size | Format | |
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tr06-06.pdf | DNB | 327.53 kB | Adobe PDF | View/Open |
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