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.

通过使用精确度和召回率评估曲线，我们提出了一种对分类器性能进行更准确评估的方法。使用基于学习矢量量化的原型分类器，我们验证了该方法在人工基准数据和数据不平衡的场景以及医学实际数据上的有效性。