Coverage-performance estimation for classification with ambiguous data

Classifier tradeoffs between accuracy and specificity are often analyzed with receiver operating curves (ROC). Here we study a related analysis of the data in terms of coverage–performance curves (CPC) which more clearly indicate the presence of ambiguous data in classification problems with overlapping class distributions. We show that feedforward mapping networks are well suited to derive such curves with minimal effort. Based on such classifiers we can identify data that need further analysis before attempting classification with sufficient confidence.