论文标题
使用列生成的NOTAM的QCodes的可解释预测
Explainable prediction of Qcodes for NOTAMs using column generation
论文作者
论文摘要
通知飞行员（NOTAM）包含重要的飞行路线相关信息。为了搜索和过滤它们，将NOTAM分组为称为QCodes的类别。在本文中，我们开发了一种工具来预测notam的QCode。我们提出了一种使用DASH，Gunluk和Wei（2018）中提出的列生成扩展可解释的二进制分类的方法，到了多类文本分类方法。我们描述了用于解决与一个VS-REST分类有关的问题，例如多个输出和类失衡。此外，我们介绍了一些启发式方法，包括使用CP-SAT求解器用于子问题，以减少训练时间。最后，我们表明我们的方法与最先进的机器学习算法（如线性SVM和小型神经网络）相比，同时添加了所需的可解释性组件。
A NOtice To AirMen (NOTAM) contains important flight route related information. To search and filter them, NOTAMs are grouped into categories called QCodes. In this paper, we develop a tool to predict, with some explanations, a Qcode for a NOTAM. We present a way to extend the interpretable binary classification using column generation proposed in Dash, Gunluk, and Wei (2018) to a multiclass text classification method. We describe the techniques used to tackle the issues related to one vs-rest classification, such as multiple outputs and class imbalances. Furthermore, we introduce some heuristics, including the use of a CP-SAT solver for the subproblems, to reduce the training time. Finally, we show that our approach compares favorably with state-of-the-art machine learning algorithms like Linear SVM and small neural networks while adding the needed interpretability component.
