The Fuzzy ROC
This work provides a method for evaluating classifiers in uncertain or fuzzy contexts, which is incremental as it builds on existing ROC analysis.
The paper tackles the problem of visualizing classification performance when some data points are not classified due to indeterminacy, by extending ROC curves to handle this scenario and addressing challenges in defining sensitivity and specificity bounds and summarizing possibilities from different indeterminacy zones.
The fuzzy ROC extends Receiver Operating Curve (ROC) visualization to the situation where some data points, falling in an indeterminacy region, are not classified. It addresses two challenges: definition of sensitivity and specificity bounds under indeterminacy; and visual summarization of the large number of possibilities arising from different choices of indeterminacy zones.