ROC analysis is increasingly being recognised as an important tool for evaluation and comparison of classifiers when the operating characteristics (i.e. class distribution and cost parameters) are not known at training time. Usually, each classifier is characterised by its estimated true and false positive rates and is represented by a single point in the ROC diagram. In this paper, we show how...

Permission is herewith granted to Università degli Studi di Cassino to circulate and to have copied for non-commercial purposes, at its discretion, the above title upon the request of individuals or institutions. Acknowledgements This work would not have been possible without the support I received from many people. A big thank you to all who have helped me in some way or other to complete this...

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 area under the ROC curve (AUC). To report it properly, it is crucial to determine an interval of confidence for its value. This paper provides confidence intervals for the AUC based on a statistical and combinatori...

Information fusion is currently a very active research topic aimed at improving the performance of biometric systems. This paper proposes a novel method for optimizing the parameters of a score fusion model based on maximizing an index related to the Area Under the ROC Curve. This approach has the convenience that the fusion parameters are learned without having to specify the client and impost...

Area Under the ROC Curve (AUC), often used for comparing classifiers, is a widely accepted performance measure for ranking instances. Many researches have studied optimization of AUC, usually via optimizing some approximation of a ranking function. Ranking SVMs are among the better performers but their usage in the literature is typically limited to learning a total ranking from partial ranking...

For many years now, there is a growing interest around ROC curve for characterizing machine learning performances. This is particularly due to the fact that in real-world problems misclassification costs are not known and thus, ROC curve and related metrics such as the Area Under ROC curve (AUC) can be a more meaningful performance measures. In this paper, we propose a SVMs based algorithm for ...

Risks exist in many different domains; medical diagnoses, financial markets, fraud detection and insurance policies are some examples. Various risk measures and risk estimation systems have hitherto been proposed and this paper suggests a new risk estimation method. Risk estimation by maximizing the area under a receiver operating characteristics (ROC) curve (REMARC) defines risk estimation as ...

The area under the receiver operating characteristic curve is frequently used as a measure for the effectiveness of diagnostic markers. In this paper we discuss and compare estimation procedures for this area. These are based on (i) the Mann-Whitney statistic; (ii) kernel smoothing; (iii) normal assumptions; (iv) empirical transformations to normality. These are compared in terms of bias and ro...

Receiver Operating Characteristic (ROC) analysis is a common tool for assessing the performance of various classification tools including biological markers, diagnostic tests, technologies or practices and statistical models. ROC analysis gained popularity in many fields including diagnostic medicine, quality control, human perception studies and machine learning. The area under the ROC curve (...