Interaction Matching for Long-Tail Multi-Label Classification
Table 1: Medical coding performance on MIMIC-III. Our results on the macro metrics for CNN and CAML are significant at p<0.05.
['Model', 'MacroP', 'MacroR', 'MacroF1', 'MicroF1']
Results. In line with prior work on the dataset, we measure the performance in terms of micro- and macro-averaged F1 score. Since our focus is improving the long tail of infrequently-occurring labels, we also include macro-averaged precision and recall. Our approach outperforms the state-of-the-art CAML method in terms of macro precision, recall and F1 by 3–5% (relative improvement, significant at p<0.05). The performance improvement on the weaker CNN baseline is even more pronounced, achieving an 8–11% improvement on the macro metrics (also significant at p<0.05). Interestingly, our approach improves the precision for the bi-directional RNN, at the expense of recall. We attribute this to the interaction matching technique that is inherently high-precision. The improvements in the micro metrics are less pronounced (and worse, in the case of micro recall on CAML), showing that our approach primarily benefits the long tail.