Area under the ROC curve, aka AUC, is a measure of choice for assessing the performance
of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that …

Support vector machines (SVMs), with their roots in Statistical Learning Theory (SLT) and
optimization methods, have become powerful tools for problem solution in machine learning …

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach,
Ensemble Methods: Foundations and Algorithms shows how these accurate methods are …

With the current massive amount of traffic that is going through the internet, internet service
providers (ISPs) and networking service providers (NSPs) are looking for various ways to …

The class imbalance problem is encountered in real-world applications of machine learning
and results in a classifier's suboptimal performance. Researchers have rigorously studied …

Most studies of online learning measure the performance of a learner by classification
accuracy, which is inappropriate for applications where the data are unevenly distributed …

AUC is an important performance measure and many algorithms have been devoted to AUC
optimization, mostly by minimizing a surrogate convex loss on a training data set. In this …

In this paper we show an efficient method for inducing classifiers that directly optimize the
area under the ROC curve. Recently, AUC gained importance in the classification …

Obesity is strongly associated with multiple risk factors. It is significantly contributing to an
increased risk of chronic disease morbidity and mortality worldwide. There are various …

AUC (area under ROC curve) is an important evaluation criterion, which has been popularly
used in many learning tasks such as class-imbalance learning, cost-sensitive learning …