Explainable prediction of Qcodes for NOTAMs using column generation

Krunal Kishor Patel, Guy Desaulniers, Andrea Lodi and Freddy Lecue

Journal of the Operational Research Society, 2024, vol. 75, issue 1, 173-183

Abstract: 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.

Date: 2024
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DOI: 10.1080/01605682.2023.2181715

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