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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks, Volume 16
Editor
Cihan H. Dagli
Cihan H. Dagli
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Anna L. Buczak
Anna L. Buczak
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David L. Enke
David L. Enke
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Mark Embrechts
Mark Embrechts
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Okan Ersoy
Okan Ersoy
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ISBN-10:
0791802566
No. of Pages:
1000
Publisher:
ASME Press
Publication date:
2006

The Receiver Operating Characteristic (ROC) curve can be thought of as a projection of a 3 dimensional trajectory through some True positive, False positive, and decision threshold space onto the True positive, False positive plane. Using this frame of reference, Alsing, et a1. (2002) developed a metric for the comparison of classifiers. It was shown that this metric appears to capture information concerning the robustness of a classifier. In this paper we investigate further the notions proposed in Alsing et al. relative to the robustness of total classification accuracy at a point of interest. Investigations are conducted with the application of several common classification methods on binary classification problems using breast cancer data from the University of Wisconsin.

Abstract
Introduction
ROC Trajectories and ROC Curves
ROC and Robustness
Finding Robust Operating Points
Investigation Methodology
Investigation Applied to Real Data
Results
Conclusions
References
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