ROC curve formulas for fused correlated classification systems

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

doi:10.1117/12.718777

7 May 2007 ROC curve formulas for fused correlated classification systems

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

Proceedings Volume 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI; 65670W (2007) https://doi.org/10.1117/12.718777
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

The Receiver Operating Characteristic (ROC) curve can be used to quantify the performance of Automatic Target Recognition (ATR) systems. When multiple classification systems are fused, the assumption of independence is usually made in order to mathematically combine the individual ROC curves for each of these classification systems into one fused ROC curve. However, correlation may exist between the classification systems and the outcomes used to generate each ROC curve. This paper will demonstrate a method for creating a ROC curve of the fused classification systems which incorporates the correlation that exists between the individual classification systems. Specifically, we will use the derived covariance between multiple classification systems to compute the existing correlation and thus the level of dependence between pairs of classification systems. Then, given a fusion rule, two systems, and the correlation between them, the ROC curve for the fused system is produced. We generate the formula for the Boolean OR and AND rules, giving the resultant ROC curve for the fused system. This paper extends our previous work in which bounds for the ROC curve of the fused, correlated classification systems were presented.

Citation Download Citation

Mark E. Oxley, Steven N. Thorsen, and Christine M. Schubert "ROC curve formulas for fused correlated classification systems", Proc. SPIE 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI, 65670W (7 May 2007); https://doi.org/10.1117/12.718777

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