Abstract
Classifier combining is a popular method for improving quality of classification – instead of using one classifier, several classifiers are organized into a classifier system and their results are aggregated into a final prediction. However, most of the commonly used aggregation methods are static, i.e., they do not adapt to the currently classified pattern. In this paper, we provide a general framework for dynamic classifier systems, which use dynamic confidence measures to adapt to a particular pattern. Our experiments with random forests on 5 artificial and 11 real-world benchmark datasets show that dynamic classifier systems can significantly outperform both confidence-free and static classifier systems.
The research presented in this paper was partially supported by the Program “Information Society” under project 1ET100300517 (D. Štefka) and by the grant No. 201/08/0802 of the Grant Agency of the Czech Republic and by the Institutional Research Plan AV0Z10300504 (M. Holeňa).
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Štefka, D., Holeňa, M. (2009). Dynamic Classifier Systems and Their Applications to Random Forest Ensembles. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2009. Lecture Notes in Computer Science, vol 5495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04921-7_47
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DOI: https://doi.org/10.1007/978-3-642-04921-7_47
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