RT Journal Article
T1 Optimum Bayesian thresholds for rebalanced classification problems using class-switching ensembles
A1 Gutiérrez López, Aitor
A1 González Serrano, Francisco Javier
A1 Figueiras, Aníbal
AB 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.
PB Elsevier
SN 0031-3203
YR 2023
FD 2023-03
LK https://hdl.handle.net/10016/38469
UL https://hdl.handle.net/10016/38469
LA eng
NO We thank the anonymous reviewers for their valuable suggestions and comments. This work is partially funded by Project PID2021-125652OB-I00 from the Ministerio de Ciencia e Innovación of Spain. Funding for APC: Universidad Carlos III de Madrid  (Read & Publish Agreement CRUE-CSIC 2022). In memoriam: Prof. Aníbal R. Figueiras-Vidal (1950-2022).
DS e-Archivo
RD 1 jul. 2024