Imbalanced learning for insurance using modified loss functions in tree-based models
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DOI: 10.1016/j.insmatheco.2022.04.010
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References listed on IDEAS
- Yi Yang & Wei Qian & Hui Zou, 2018. "Insurance Premium Prediction via Gradient Tree-Boosted Tweedie Compound Poisson Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(3), pages 456-470, July.
- Roel Henckaerts & Marie-Pier Côté & Katrien Antonio & Roel Verbelen, 2021. "Boosting Insights in Insurance Tariff Plans with Tree-Based Machine Learning Methods," North American Actuarial Journal, Taylor & Francis Journals, vol. 25(2), pages 255-285, April.
- Lopez, Olivier & Milhaud, Xavier & Thérond, Pierre-E., 2019. "A Tree-Based Algorithm Adapted To Microlevel Reserving And Long Development Claims," ASTIN Bulletin, Cambridge University Press, vol. 49(3), pages 741-762, September.
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Cited by:
- Yaojun Zhang & Lanpeng Ji & Georgios Aivaliotis & Charles Taylor, 2023. "Bayesian CART models for insurance claims frequency," Papers 2303.01923, arXiv.org, revised Dec 2023.
- Zhiyu Quan & Changyue Hu & Panyi Dong & Emiliano A. Valdez, 2024. "Improving Business Insurance Loss Models by Leveraging InsurTech Innovation," Papers 2401.16723, arXiv.org.
- Zhang, Yaojun & Ji, Lanpeng & Aivaliotis, Georgios & Taylor, Charles, 2024. "Bayesian CART models for insurance claims frequency," Insurance: Mathematics and Economics, Elsevier, vol. 114(C), pages 108-131.
- Yang Qiao & Chou-Wen Wang & Wenjun Zhu, 2024. "Machine learning in long-term mortality forecasting," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 340-362, April.
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More about this item
Keywords
Predictive model of insurance claims; Imbalanced learning; Custom loss; Canberra distance; Regression tree; Tree-based algorithms;All these keywords.
JEL classification:
- G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
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