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.
@article{arxiv.2208.04955,
title = {Explainable prediction of Qcodes for NOTAMs using column generation},
author = {Krunal Kishor Patel and Guy Desaulniers and Andrea Lodi and Freddy Lecue},
journal= {arXiv preprint arXiv:2208.04955},
year = {2023}
}