Explainable prediction of Qcodes for NOTAMs using column generation
提出一种基于列生成的可解释多分类方法，用于预测航空通告（NOTAM）的Q代码，在保持与线性SVM等算法相当性能的同时提供解释性。
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 (Citation2018) 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.