Quantifying the performance of fused correlated multiple classifiers

Christine M. Schubert; Mark E. Oxley; Kenneth W. Bauer Jr.

doi:10.1117/12.602956

25 May 2005 Quantifying the performance of fused correlated multiple classifiers

Christine M. Schubert, Mark E. Oxley, Kenneth W. Bauer Jr.

Proceedings Volume 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV; (2005) https://doi.org/10.1117/12.602956
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

The Receiver Operating Characteristic (ROC) curve is typically used to quantify the performance of Automatic Target Recognition (ATR) systems. When multiple classifiers are to be fused, assumptions must be made in order to mathematically combine the individual ROC curves for each of these classifiers in order to form one fused ROC curve. Often, one of these assumptions is independence between the classifiers. However, correlation may exist between the classifiers, processors, sensors and the outcomes used to generate each ROC curve. This paper will demonstrate a method for creating a ROC curve of the fused classifiers which incorporates the correlation that exists between the individual ROC curves. Specifically, we will use the derived covariance matrix between multiple classifiers to compute the existing correlation and level of dependence between pairs of classifiers. The ROC curve for the fused system is then produced, adjusting for this level of dependency, using a given fusion rule.

Citation Download Citation

Christine M. Schubert, Mark E. Oxley, and Kenneth W. Bauer Jr. "Quantifying the performance of fused correlated multiple classifiers", Proc. SPIE 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV, (25 May 2005); https://doi.org/10.1117/12.602956

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