Precision and Recall Reject Curves for Classification
This work addresses the need for better evaluation metrics in classification scenarios with imbalanced data, offering a domain-specific improvement for fields like medical analysis.
The paper tackles the problem of evaluating classifier performance when rejecting uncertain predictions, proposing precision- and recall-reject curves as alternatives to accuracy-reject curves, and shows they provide more accurate insights on imbalanced benchmarks and medical data.
For some classification scenarios, it is desirable to use only those classification instances that a trained model associates with a high certainty. To obtain such high-certainty instances, previous work has proposed accuracy-reject curves. Reject curves allow to evaluate and compare the performance of different certainty measures over a range of thresholds for accepting or rejecting classifications. However, the accuracy may not be the most suited evaluation metric for all applications, and instead precision or recall may be preferable. This is the case, for example, for data with imbalanced class distributions. We therefore propose reject curves that evaluate precision and recall, the recall-reject curve and the precision-reject curve. Using prototype-based classifiers from learning vector quantization, we first validate the proposed curves on artificial benchmark data against the accuracy reject curve as a baseline. We then show on imbalanced benchmarks and medical, real-world data that for these scenarios, the proposed precision- and recall-curves yield more accurate insights into classifier performance than accuracy reject curves.