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

publication date

  • March 2023

start page

  • 1

end page

  • 8


  • 109158


  • 135

International Standard Serial Number (ISSN)

  • 0031-3203


  • 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.


  • Telecommunications


  • bayesian framework; ensembles; rebalancing techniques; imbalanced classification; label switching