Quantifying the robustness of classification systems

Steven N. Thorsen; Mark E. Oxley

doi:10.1117/12.666687

17 May 2006 Quantifying the robustness of classification systems

Steven N. Thorsen, Mark E. Oxley

Proceedings Volume 6235, Signal Processing, Sensor Fusion, and Target Recognition XV; 62350W (2006) https://doi.org/10.1117/12.666687
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

Automatic Target Recognition (ATR) system's performance is quantified using Receiver Operating Characteristic (ROC) curves (or ROC manifolds for more than two labels) and typically the prior probabilities of each labeled-event occurring. In real-world problems, one does not know the prior probabilities and they have to be approximated or guessed, but usually one knows their range or distribution. We derive an objective functional that quantifies the robustness of an ATR system given: (1) a set of prior probabilities, and (2) a distribution of a set of prior probabilities. The ATR system may have two labels or more. We demonstrate the utility of this objective functional with examples, and show how it can be used to determine the optimal ATR system from a family of systems.

Citation Download Citation

Steven N. Thorsen and Mark E. Oxley "Quantifying the robustness of classification systems", Proc. SPIE 6235, Signal Processing, Sensor Fusion, and Target Recognition XV, 62350W (17 May 2006); https://doi.org/10.1117/12.666687

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