用响应面法优化保险神经网络超参，效率提升近三成
Optimization of Actuarial Neural Networks with Response Surface Methodology
- 采用因子设计与响应面法系统探索超参数空间
- 减少288次实验至188次，出样本泊松偏差损失近乎最优
- 可为保险建模者提供高效超参调优工具
在数据驱动的精算科学中，机器学习在预测建模中发挥关键作用，提升了风险评估与定价策略。神经网络，尤其是结合型精算神经网络（CANN），对死亡率预测和定价等任务至关重要。然而，超参数（如学习率、层数）的优化对资源效率极为重要。本研究采用因子设计与响应面方法（RSM）优化CANN性能。RSM能有效探索超参数空间并捕捉潜在曲率，优于传统网格搜索。结果表明，该方法可准确预测性能，识别出关键超参数。通过剔除统计不显著的超参数，实验次数从288次降至188次，精度损失可忽略，实现了近最优的出样本泊松偏差损失。
原文摘要 · Abstract (English)
In the data-driven world of actuarial science, machine learning (ML) plays a crucial role in predictive modeling, enhancing risk assessment and pricing strategies. Neural networks, specifically combined actuarial neural networks (CANN), are vital for tasks such as mortality forecasting and pricing. However, optimizing hyperparameters (e.g., learning rates, layers) is essential for resource efficiency. This study utilizes a factorial design and response surface methodology (RSM) to optimize CANN performance. RSM effectively explores the hyperparameter space and captures potential curvature, outperforming traditional grid search. Our results show accurate performance predictions, identifying critical hyperparameters. By dropping statistically insignificant hyperparameters, we reduced runs from 288 to 188, with negligible loss in accuracy, achieving near-optimal out-of-sample Poisson deviance loss.
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