用重采样与GAN提升保险巨灾数据质量
Improving Insurance Catastrophic Data with Resampling and GAN Methods
- 结合自助法、刀切法与GAN生成高质量巨灾数据
- 实测显示新方法使误差降低，均方误差更小
- 适合需要高精度风险评估的保险机构
精确且大规模的巨灾事件数据对保险公司至关重要。为提升此类数据质量，本文提出基于自助法（bootstrap）、刀切法（bootknife）和生成对抗网络（GAN）的三种方法。通过数值实验与真实数据对比，评估其在均方误差（MSE）和平均绝对误差（MAE）上的表现。此外，还设计了一种直接算法，用于构建模糊专家意见以辅助结果判断。
原文摘要 · Abstract (English)
The precise and large dataset concerning catastrophic events is very important for insurers. To improve the quality of such data three methods based on the bootstrap, bootknife, and GAN algorithms are proposed. Using numerical experiments and real-life data, simulated outputs for these approaches are compared based on the mean squared (MSE) and mean absolute errors (MAE). Then, a direct algorithm to construct a fuzzy expert's opinion concerning such outputs is also considered.
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