Predicting Classification Accuracy When Adding New Unobserved Classes
This work addresses the challenge of estimating classifier performance in real-world scenarios where not all classes are known during training, which is incremental but improves practical evaluation in domains like object detection and face recognition.
The paper tackles the problem of predicting a multiclass classifier's accuracy on unobserved classes by introducing the 'reversed ROC' (rROC) measure and a neural-network-based algorithm called CleaneX, which achieves significantly better predictions than current state-of-the-art methods on simulations and real datasets.
Multiclass classifiers are often designed and evaluated only on a sample from the classes on which they will eventually be applied. Hence, their final accuracy remains unknown. In this work we study how a classifier's performance over the initial class sample can be used to extrapolate its expected accuracy on a larger, unobserved set of classes. For this, we define a measure of separation between correct and incorrect classes that is independent of the number of classes: the "reversed ROC" (rROC), which is obtained by replacing the roles of classes and data-points in the common ROC. We show that the classification accuracy is a function of the rROC in multiclass classifiers, for which the learned representation of data from the initial class sample remains unchanged when new classes are added. Using these results we formulate a robust neural-network-based algorithm, "CleaneX", which learns to estimate the accuracy of such classifiers on arbitrarily large sets of classes. Unlike previous methods, our method uses both the observed accuracies of the classifier and densities of classification scores, and therefore achieves remarkably better predictions than current state-of-the-art methods on both simulations and real datasets of object detection, face recognition, and brain decoding.