The area under the ROC (receiver operating characteristics) curve, or simply AUC, has been
traditionally used in medical diagnosis since the 1970s. It has recently been proposed as an …

This paper is about constructing confidence bands around ROC curves. We first introduce to
the machine learning community three band-generating methods from the medical field, and …

In many applications, good ranking is a highly desirable performance for a classifier. The
criterion commonly used to measure the ranking quality of a classification algorithm is the …

Predictive accuracy has been widely used as the main criterion for comparing the predictive
ability of classification systems (such as C4. 5, neural networks, and Naive Bayes). Most of …

The area under the ROC curve, or AUC, has been widely used to assess the ranking
performance of binary scoring classifiers. Given a sample, the metric considers the ordering …

We present a statistical analysis of the AUC as an evaluation criterion for classification
scoring models. First, we consider significance tests for the difference between AUC scores …

The field of machine learning has matured to the point where many sophisticated learning
approaches can be applied to practical applications. Thus it is of critical importance that …

The proportion of successful hits, usually referred to as “accuracy”, is by far the most
dominant meter for measuring classifiers' accuracy. This is despite of the fact that accuracy …

In 1988, Langley wrote an influential editorial in the journal Machine Learning titled
“Machine Learning as an Experimental Science”, arguing persuasively for a greater focus on …

The selection of the best classification algorithm for a given dataset is a very widespread
problem. It is also a complex one, in the sense it requires to make several important …