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Information theoretic combination of pattern classifiers
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DOI：10.1016/j.patcog.2010.04.013.png)
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摘要
En 中文
Combining several classifiers has proved to be an effective machine learning technique. Two concepts clearly influence the performances of an ensemble of classifiers: the diversity between classifiers and the individual accuracies of the classifiers. In this paper we propose an information theoretic framework to establish a link between these quantities. As they appear to be contradictory, we propose an information theoretic score (ITS) that expresses a trade-off between individual accuracy and diversity. This technique can be directly used, for example, for selecting an optimal ensemble in a pool of classifiers. We perform experiments in the context of overproduction and selection of classifiers, showing that the selection based on the ITS outperforms state-of-the-art diversity-based selection techniques. (C) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Machine learning
Pattern recognition
Classifier combination
Information theory
Mutual information
Diversity
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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