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Vote counting measures for ensemble classifiers
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DOI：10.1016/S0031-3203(03)00191-2.png)
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摘要
En 中文
Various measures, such as Margin and Bias/Variance, have been proposed with the aim of gaining a better understanding of why Multiple Classifier Systems (MCS) perform as well as they do. While these measures provide different perspectives for MCS analysis, it is not clear how to use them for MCS design. In this paper a different measure based on a spectral representation is proposed for two-class problems. It incorporates terms representing positive and negative correlation of pairs of training patterns with respect to class labels. Experiments employing MLP base classifiers, in which parameters are fixed but systematically varied, demonstrate the sensitivity of the proposed measure to base classifier complexity. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keyword:
decision level fusion
multiple classifiers
ensembles
error-correcting
binary coding
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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