Confidence of a ROC manifold

Mark E. Oxley; Christine M. Schubert; Steven N. Thorsen

doi:10.1117/12.850892

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27 April 2010 Confidence of a ROC manifold

Mark E. Oxley, Christine M. Schubert, Steven N. Thorsen

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Proceedings Volume 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX; 76970T (2010) https://doi.org/10.1117/12.850892
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

A Classification system such as an Automatic Target Recognition (ATR) system with N possible output labels (or decisions) will have N(N-1) possible errors. The Receiver Operating Characteristic (ROC) manifold was created to quantify all of these errors. Finite truth data will produce an approximation to a ROC manifold. How well does the approximate ROC manifold approximate the TRUE ROC manifold? Several metrics exist that quantify the approximation ability, but researchers really wish to quantify the confidence in the approximate ROC manifold. This paper will review different confidence definitions for ROC curves and will derive an expression for confidence of a ROC manifold. The foundation of the confidence expression is based upon the Chebychev inequality..

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Mark E. Oxley, Christine M. Schubert, and Steven N. Thorsen "Confidence of a ROC manifold", Proc. SPIE 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX, 76970T (27 April 2010); https://doi.org/10.1117/12.850892

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