驾驶员遥测综合数据集的生成
Synthetic Dataset Generation of Driver Telematics
摘要：
本文介绍了在生产的驾驶员远程信息处理模拟从一个类似的真实保险数据集的合成数据集所采用的技术。生成的合成数据集包含100，000份保单，其中包括对驾驶员索赔经验的观察以及相关的经典风险变量和远程信息处理相关变量。这项工作的目的是产生一个资源，可用于推进模型，以评估基于使用的保险的风险。它遵循一个使用机器学习算法的三阶段过程。第一阶段是应用前馈神经网络将索赔数量的值模拟为多个二元分类。第二阶段是使用前馈神经网络模拟索赔总额的值作为回归，索赔数量包括在特征变量集中。在最后阶段，应用扩展的$\texttt{SMOTE}$算法生成特征变量空间的合成组合。当泊松和伽马回归模型拟合到相应的数据时，通过比较合成数据集和真实数据集来评估所得到的数据集。其他可视化和数据汇总在两个数据集之间产生了显著相似的统计数据。我们希望有兴趣获得远程信息处理数据集来校准模型或学习算法的研究人员会发现我们的工作很有价值。
英文摘要：
This article describes techniques employed in the production of a synthetic dataset of driver telematics emulated from a similar real insurance dataset. The synthetic dataset generated has 100,000 policies that included observations about driver's claims experience together with associated classical risk variables and telematics-related variables. This work is aimed to produce a resource that can be used to advance models to assess risks for usage-based insurance. It follows a three-stage process using machine learning algorithms. The first stage is simulating values for the number of claims as multiple binary classifications applying feedforward neural networks. The second stage is simulating values for aggregated amount of claims as regression using feedforward neural networks, with number of claims included in the set of feature variables. In the final stage, a synthetic portfolio of the space of feature variables is generated applying an extended $\texttt{SMOTE}$ algorithm. The resulting dataset is evaluated by comparing the synthetic and real datasets when Poisson and gamma regression models are fitted to the respective data. Other visualization and data summarization produce remarkable similar statistics between the two datasets. We hope that researchers interested in obtaining telematics datasets to calibrate models or learning algorithms will find our work valuable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
