Interpretable Multiclass Text Classification Using Column Generation

In this presentation, we start by discussing a binary classification model for interpretable boolean decision rule generation by Dash et al. 2018 that is solved using column generation. We then talk about how we extended it to a multiclass text classification framework for a prediction problem in the aviation industry, where we needed to classify a set of text messages (NOTAMs) into a specific set of categories (Qcodes). Specifically, we present the techniques we used to tackle the issues related to one-vs-rest classification such as multiple outputs, class imbalances etc. We also talk about using a CP-SAT solver as a heuristic to speed up the training process. Finally, we conclude the presentation with the comparison of our results with the results of some standard machine learning algorithms, and the discussion of future ideas we want to implement for this task.