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Published in:
2009 | OriginalPaper | Chapter
Dynamic Classifier Systems and Their Applications to Random Forest Ensembles
Authors : David Štefka, Martin Holeňa
Published in: Adaptive and Natural Computing Algorithms
Publisher: Springer Berlin Heidelberg
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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.