Skip to Main content Skip to Navigation
Book sections

Empirical performance maximization for linear rank statistics

Abstract : The ROC curve is known to be the golden standard for measuring performance of a test/scoring statistic regarding its capacity of discrimination between two populations in a wide variety of applications, ranging from anomaly detection in signal processing to information retrieval, through medical diagnosis. Most practical performance measures used in scoring applications such as the AUC, the local AUC, the p-norm push, the DCG and others, can be seen as summaries of the ROC curve. This paper highlights the fact that many of these empirical criteria can be expressed as (conditional) linear rank statistics. We investigate the properties of empirical maximizers of such performance criteria and provide preliminary results for the concentration properties of a novel class of random variables that we will call a linear rank process.
Complete list of metadata

Cited literature [14 references]  Display  Hide  Download
Contributor : Stephan Clémençon Connect in order to contact the contributor
Submitted on : Tuesday, April 23, 2019 - 3:32:55 PM
Last modification on : Tuesday, October 19, 2021 - 11:14:12 AM


Files produced by the author(s)


  • HAL Id : hal-02107196, version 1


Stéphan Clémençon, Nicolas Vayatis. Empirical performance maximization for linear rank statistics. Empirical performance maximization for linear rank statistics, 2009, Advances in Neural Information Processing Systems 21. ⟨hal-02107196⟩



Record views


Files downloads