Optimum Bayesian thresholds for rebalanced classification problems using class-switching ensembles

•Imbalance can be mitigated by rebalancing (costs, population) or ensemble learning.•Asymmetric label switching creates diversity in ensemble learning.•Rebalancing and switching can be combined in a principled way.•Optimum decision thresholds for these combinations are analytically derived.•A gating...

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Bibliographic Details
Published in:Pattern recognition 2023-03, Vol.135, p.109158, Article 109158
Main Authors: Gutiérrez-López, Aitor, González-Serrano, Francisco-Javier, Figueiras-Vidal, Aníbal R.
Format: Article
Language:English
Subjects:
Bayesian framework
Ensembles
Imbalanced classification
Label switching
Rebalancing techniques
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Summary:•Imbalance can be mitigated by rebalancing (costs, population) or ensemble learning.•Asymmetric label switching creates diversity in ensemble learning.•Rebalancing and switching can be combined in a principled way.•Optimum decision thresholds for these combinations are analytically derived.•A gating network aggregating the learners contributions improves performance. 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.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2022.109158