一种用于静态无路线到达时间估计的可解释堆叠集成模型
An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival
AI总结：
本文提出一种新颖的两级堆叠集成模型用于静态无路线ETA预测，并结合三种XAI解释连接方法，在提升预测精度的同时增强模型可解释性。
摘要：
为了比较备选出租车调度方案并对其进行计算，以及向司机和乘客提供即将到来的出租车行程的洞察，需要预测行程持续时间或其估计到达时间（ETA）。为了达到较高的预测精度，用于ETA的机器学习模型是当前最先进的技术。进一步提高预测精度的一个尚未被利用的选择是将多个ETA模型组合成一个集成模型。虽然预测精度的提高是可能的，但主要缺点是，由于复杂的集成架构，这种集成模型所做的预测变得不那么透明。弥补这一缺点的一种选择是应用可解释人工智能（XAI）。本文的贡献有三个方面。首先，我们将我们之前工作中用于ETA的多个机器学习模型组合成一个两级集成模型——即堆叠集成模型——这本身是新颖的；因此，我们能够超越先前最先进的静态无路线ETA方法。其次，我们应用现有的XAI方法来解释该集成的第一级和第二级模型。第三，我们提出了三种连接方法，用于将第一级解释与第二级解释结合起来。这些连接方法使我们能够为回归任务解释堆叠集成模型。实验评估表明，ETA模型正确地学习到了那些驱动预测的输入特征的重要性。
英文摘要：
To compare alternative taxi schedules and to compute them, as well as to provide insights into an upcoming taxi trip to drivers and passengers, the duration of a trip or its Estimated Time of Arrival (ETA) is predicted. To reach a high prediction precision, machine learning models for ETA are state of the art. One yet unexploited option to further increase prediction precision is to combine multiple ETA models into an ensemble. While an increase of prediction precision is likely, the main drawback is that the predictions made by such an ensemble become less transparent due to the sophisticated ensemble architecture. One option to remedy this drawback is to apply eXplainable Artificial Intelligence (XAI). The contribution of this paper is three-fold. First, we combine multiple machine learning models from our previous work for ETA into a two-level ensemble model - a stacked ensemble model - which on its own is novel; therefore, we can outperform previous state-of-the-art static route-free ETA approaches. Second, we apply existing XAI methods to explain the first- and second-level models of the ensemble. Third, we propose three joining methods for combining the first-level explanations with the second-level ones. Those joining methods enable us to explain stacked ensembles for regression tasks. An experimental evaluation shows that the ETA models correctly learned the importance of those input features driving the prediction.
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。
