Calibrated, cost-sensitive, explainable ensemble learning for customer churn prediction in retail banking: a leakage-safe pipeline and single-dataset case study.
- DOI
- 10.3389/frai.2026.1889639
- Published
- 2026
- Container
- Frontiers in artificial intelligence
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3389/frai.2026.1889639,
title = {Calibrated, cost-sensitive, explainable ensemble learning for customer churn prediction in retail banking: a leakage-safe pipeline and single-dataset case study.},
author = {Karim F and Alam MI and Lalafaryan A and Mkrtchyan A and Hovakimyan A and Mustafizur M and Alam GT and Manik MMTG},
year = {2026},
journal = {Frontiers in artificial intelligence},
doi = {10.3389/frai.2026.1889639},
url = {https://doi.org/10.3389/frai.2026.1889639}
}RIS
TY - JOUR TI - Calibrated, cost-sensitive, explainable ensemble learning for customer churn prediction in retail banking: a leakage-safe pipeline and single-dataset case study. AU - Karim F AU - Alam MI AU - Lalafaryan A AU - Mkrtchyan A AU - Hovakimyan A AU - Mustafizur M AU - Alam GT AU - Manik MMTG PY - 2026 JO - Frontiers in artificial intelligence DO - 10.3389/frai.2026.1889639 UR - https://doi.org/10.3389/frai.2026.1889639 ER -
APA
F, K., MI, A., A, L., A, M., A, H., M, M., GT, A., & MMTG, M. (2026). Calibrated, cost-sensitive, explainable ensemble learning for customer churn prediction in retail banking: a leakage-safe pipeline and single-dataset case study.. Frontiers in artificial intelligence. https://doi.org/10.3389/frai.2026.1889639
Source records
- pubmed · retrieved 2026-11-10T20:56:29.417Z