Characterising the Area Under the Curve Loss Function Landscape

Estimating a Bounded Normal Mean Under the LINEX Loss Function

Let X be a random variable from a normal distribution with unknown mean θ and known variance σ2. In many practical situations, θ is known in advance to lie in an interval, say [−m,m], for some m > 0. As the usual estimator of θ, i.e., X under the LINEX loss function is inadmissible, finding some competitors for X becomes worthwhile. The only study in the literature considered the problem of min...

Boosting the Area under the ROC Curve

We show that any weak ranker that can achieve an area under the ROC curve slightly better than 1/2 (which can be achieved by random guessing) can be efficiently boosted to achieve an area under the ROC curve arbitrarily close to 1. We further show that this boosting can be performed even in the presence of independent misclassification noise, given access to a noise-tolerant weak ranker.

Estimation of the Multivariate Normal Mean under the Extended Reflected Normal Loss Function

Improving the Sensitivity of Research Publication Indices Using Exponential Function and the Area under Curve

In collecting and analyzing information about recent research papers, authors, and journals with their citation and indices, we noticed that in many cases such indices fail to reflect reality and are incapable to distinguish between different authors in some cases while they clearly have different research profiles. There are many research papers that can be found in literature that discussed t...

Generalization Bounds for the Area Under the ROC Curve

We study generalization properties of the area under the ROC curve (AUC), a quantity that has been advocated as an evaluation criterion for the bipartite ranking problem. The AUC is a different term than the error rate used for evaluation in classification problems; consequently, existing generalization bounds for the classification error rate cannot be used to draw conclusions about the AUC. I...