论文标题
通过从异常检测衍生的常规和特殊性曲线提取特征分类的索赔分类
Enhancing Claim Classification with Feature Extraction from Anomaly-Detection-Derived Routine and Peculiarity Profiles
论文作者
论文摘要
基于用法的保险已成为车辆保险的新标准；因此，找到使用保险人的驾驶数据的有效方法很重要。在车辆的行程摘要中应用异常检测，我们开发了一种方法，允许为每辆车辆得出“常规”和“特殊性”异常轮廓。为此，使用每辆车辆进行的每次旅行的异常检测算法来计算例程和特殊性异常得分。与相关车辆进行的其他旅行相比，前者测量了旅行的异常程度，而与任何车辆进行的旅行相比，后者衡量其异常程度。所得的异常得分向量用作常规和特殊性曲线。然后从这些配置文件中提取功能，为此我们研究索赔分类框架中的预测能力。使用实际数据，我们发现从车辆的特殊性概况提取的功能改善了分类。
Usage-based insurance is becoming the new standard in vehicle insurance; it is therefore relevant to find efficient ways of using insureds' driving data. Applying anomaly detection to vehicles' trip summaries, we develop a method allowing to derive a "routine" and a "peculiarity" anomaly profile for each vehicle. To this end, anomaly detection algorithms are used to compute a routine and a peculiarity anomaly score for each trip a vehicle makes. The former measures the anomaly degree of the trip compared to the other trips made by the concerned vehicle, while the latter measures its anomaly degree compared to trips made by any vehicle. The resulting anomaly scores vectors are used as routine and peculiarity profiles. Features are then extracted from these profiles, for which we investigate the predictive power in the claim classification framework. Using real data, we find that features extracted from the vehicles' peculiarity profile improve classification.
