Many problems involve the prediction of multiple, possibly dependent labels. The structured output prediction framework builds predictors that take these dependencies into account and use them to improve accuracy. In many such tasks, performance is evaluated by the Area Under the ROC Curve (AUC). While a framework for optimizing the AUC loss for unstructured models exists, it does not naturally...

Credit scoring models are very important tools for financial institutions to make credit granting decisions. In the last few decades, many quantitative methods have been used for the development of credit scoring models with focus on maximizing classification accuracy. This paper proposes the credit scoring models with the area under receiver operating characteristics curve (AUC) maximization b...

Longitudinal data are very common in biomedical and clinical research, for example, CD4+ cell responses and viral load responses in AIDS clinical research. It is challenging to do inference for the whole trajectory of these longitudinal data if a parametric function is not available to model the trajectories. In this paper we develop an area-under-the-curve (AUC) based nonparametric method to c...

Reliable estimation of the classification performance of learned predictive models is difficult, when working in the small sample setting. When dealing with biological data it is often the case that separate test data cannot be afforded. Cross-validation is in this case a typical strategy for estimating the performance. Recent results, further supported by experimental evidence presented in thi...

Identifying genes (biomarkers) and predicting the clinical outcomes with censored survival times are important for cancer prognosis and pathogenesis. In this article, we propose a novel method with L(1) penalized global AUC summary maximization (L(1)GAUCS). The L(1)GAUCS method is developed for simultaneous gene (feature) selection and survival prediction. L(1) penalty shrinks coefficients and ...

Recent developments in molecular biology have led to the massive discovery of new marker candidates for the prediction of patient survival. To evaluate the predictive value of these markers, statistical tools for measuring the performance of survival models are needed. We consider estimators of discrimination measures, which are a popular approach to evaluate survival predictions in biomarker s...

BACKGROUND Despite differences in types of infection and causative organisms, pharmacokinetic-pharmacodynamic (PKPD) targets of vancomycin therapy derived from adult studies are suggested for neonates. We aimed to identify doses needed for the attainment of AUC/MIC > 400 and AUC/MIC > 300 in neonates with sepsis and correlate these targets with recommended doses and treatment outcome. METHODS...

Cytochrome P450 (CYP) 2C19 (CYP2C19) genotype is regarded as a useful tool to predict area under the blood concentration-time curve (AUC) of proton pump inhibitors (PPIs). In our results, however, CYP2C19 genotypes had no influence on AUC of all PPIs during fluvoxamine treatment. These findings suggest that CYP2C19 genotyping is not always a good indicator for estimating AUC of PPIs. Limited sa...

The area under the ROC curve (AUC) is a widely used performance measure in machine learning. Increasingly, however, in several applications, ranging from ranking to biometric screening to medicine, performance is measured not in terms of the full area under the ROC curve but in terms of the partial area under the ROC curve between two false-positive rates. In this letter, we develop support vec...

MOTIVATION The area under the receiver operating characteristic (ROC) curve (AUC), long regarded as a 'golden' measure for the predictiveness of a continuous score, has propelled the need to develop AUC-based predictors. However, the AUC-based ensemble methods are rather scant, largely due to the fact that the associated objective function is neither continuous nor concave. Indeed, there is no ...