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Optimum Bayesian thresholds for rebalanced classification problems using class-switching ensembles
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DOI：10.1016/j.patcog.2022.109158.png)
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摘要
En 中文
Asymmetric label switching is an effective and principled method for creating a diverse ensemble of learners for imbalanced classification problems. This technique can be combined with other rebalancing mechanisms, such as those based on cost policies or class proportion modifications. In this study, and under the Bayesian theory framework, we specify the optimal decision thresholds for the combination of these mechanisms. In addition, we propose using a gating network to aggregate the learners contributions as an additional mechanism to improve the overall performance of the system.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
Keyword:
Bayesian framework
Ensembles
Rebalancing techniques
Imbalanced classification
Label switching
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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The impact of class imbalance in classification performance metrics based on the binary confusion matrix.png)
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