Arbeitspapier
Boosting Classifiers for Drifting Concepts
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.
- Language
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Englisch
- Bibliographic citation
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Series: Technical Report ; No. 2006,06
- Event
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Geistige Schöpfung
- (who)
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Scholz, Martin
Klinkenberg, Ralf
- Event
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Veröffentlichung
- (who)
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Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
- (where)
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Dortmund
- (when)
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2006
- Handle
- Last update
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10.03.2425, 11:45 AM CET
Data provider
This object is provided by:
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Scholz, Martin
- Klinkenberg, Ralf
- Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Time of origin
- 2006
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