The AUC can be intuitively motivated by the motivation that if one ROC curve lies strictly above another then the respective classifier performs better at all thresholds levels
The AUC is an aggregate or portmanteau measure equivalent to integrating over a range of possible values for the threshold
The error rate is simply the weighted average with the weights given by the class proportions in the population
To overcome the deficiency of the AUC, the H measure is proposed using a fixed relative misclassification severity distribution
The H measure requires that the same w distribution is used for all classifiers
In highly unbalanced situations, one regards it as more likely that misclassifications from the smaller class will be more serious that misclassifications from the larger class
The ratio of the costs of the two types of misclassifications errors is given by r=c/(1-c) where r measures how much more severe misclassifying a class 0 instance is than misclassifying a class 1 instance
The H measure should be presented with two forms of distributions: first a subjective distribution and second a universal standard distribution with a beta of Beta(2,2)
