Boosting cost-complexity pruned trees on Tweedie responses: the ABT machine for insurance ratemaking

References

• Breiman, L., Friedman, J. H., Olshen, R. A. & Stone, C. J. (1984). Classification and Regression Trees. Boca Raton, FL: CRC Press.
   Google Scholar
• Chen, H., Tino, P. & Yao, X. (2009). Predictive ensemble pruning by expectation propagation. IEEE Transactions on Knowledge and Data Engineering 21(7), 999–1013.
   Web of Science ®Google Scholar
• Costa, V. G. & Pedreira, C. E. (2022). Recent advances in decision trees: An updated survey. Artificial Intelligence Review 56(5), 4765–4800.
   Web of Science ®Google Scholar
• Denuit, M., Hainaut, D. & Trufin, J. (2019a). Effective statistical learning methods for actuaries I: GLM and extensions. Springer Actuarial Lecture Notes Series. Cham: Springer.
   Google Scholar
• Denuit, M., Hainaut, D. & Trufin, J. (2019b). Effective statistical learning methods for actuaries III: Neural networks and extensions. Springer Actuarial Lecture Notes Series. Cham: Springer.
   Google Scholar
• Denuit, M., Hainaut, D. & Trufin, J. (2020). Effective statistical learning methods for actuaries II: Tree-based methods and extensions. Springer Actuarial Lecture Notes Series. Cham: Springer.
   Google Scholar
• Friedman, J. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics 29(5), 1189–1232.
   Web of Science ®Google Scholar
• Garcia Leiva, R., Fernandez Anta, A. & Mancuso, V. (2019). A novel hyperparameter-free approach to decision tree construction that avoids overfitting by design. IEEE Access 7, 99978–99987.
   Web of Science ®Google Scholar
• Guelman, L. (2012). Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Systems with Applications 39, 3659–3667.
   Web of Science ®Google Scholar
• Hainaut, D., Trufin, J. & Denuit, M. (2022). Response versus gradient boosting trees, GLMs and neural networks under Tweedie loss and log-link. Scandinavian Actuarial Journal 2022(10), 841–866.
   Web of Science ®Google Scholar
• Henckaerts, R., Cote, M.-P., Antonio, K. & Verbelen, R. (2021). Boosting insights in insurance tariff plans with tree-based machine learning methods. North American Actuarial Journal 25(2), 255–285.
   Web of Science ®Google Scholar
• Hernandez-Lobato, D., Hernandez-Lobato, J. M., Ruiz-Torrubiano, R. & Valle, A. (2006). Pruning adaptive boosting ensembles by means of a genetic algorithm. Intelligent Data Engineering and Automated Learning. Proceedings 7. Berlin: Springer. P. 322–329.
   Google Scholar
• Lee, S. C. & Lin, S. (2018). Delta boosting machine with application to general insurance. North American Actuarial Journal 22, 405–425.
   Web of Science ®Google Scholar
• Margineantu, D. D. & Dietterich, T. G. (1997). Pruning adaptive boosting. International Conference on Machine Learning, San Francisco, Vol. 97. P. 211–218.
   Google Scholar
• Noll, A., Salzmann, R. & Wüthrich, M. (2018). Case study: French motor third-party liability claims. Available at SSRN: https://ssrn.com/abstract=3164764.
   Google Scholar
• Pesantez-Narvaez, J., Guillen, M. & Alcaniz, M. (2019). Predicting motor insurance claims using telematics data-XGBoost versus logistic regression. Risks 7, 1–16.
   Web of Science ®Google Scholar
• Quinlan, J. R. (1986). Induction of decision trees. Machine Learning 1, 81–106.
   Google Scholar
• Tamon, C. & Xiang, J. (2000). On the boosting pruning problem. European Conference on Machine Learning, Proceedings. Berlin: Springer, Vol. 11, P. 404–412.
   Google Scholar
• Thompson, S. (1999). Pruning boosted classifiers with a real valued genetic algorithm. Knowledge-Based Systems 12(5-6), 277–284.
   Web of Science ®Google Scholar
• Vidal, T. & Schiffer, M. (2020). Born-again tree ensembles. In International Conference on Machine Learning. Proceedings of the 37th International Conference on Machine Learning, PMLR 119, P. 9743–9753.
   Google Scholar
• Windeatt, T. & Ardeshir, G. (2001). An empirical comparison of pruning methods for ensemble classifiers. In Advances in Intelligent Data Analysis: 4th International Conference. Berlin, Heidelberg: Springer.
   Google Scholar
• Wu, C. C., Chen, Y. L. & Liu, Y. H. (2016). Decision tree induction with a constrained number of leaf nodes. Applied Intelligence 45(3), 673–685.
   Web of Science ®Google Scholar
• Wüthrich, M. V. & Buser, C. (2019). Data analytics for non-life insurance pricing. Lecture notes available at SSRN http://doi.org/10.2139/ssrn.2870308.
   Google Scholar
• Yang, Y., Qian, W. & Zou, H. (2018). Insurance premium prediction via gradient tree-boosted Tweedie compound Poisson models. Journal of Business & Economic Statistics 36, 456–470.
   Web of Science ®Google Scholar