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Abstract: It is well understood that binary classifiers have two implicit objective functions (sensitivity and specificity) describing their performance. Traditional methods of classifier training attempt to combine these two objective functions (or two analogous class performance measures) into one so that conventional scalar optimization techniques can be utilized. This involves incorporating a priori information into the aggregation method so that the resulting performance of the classifier is... (Update)
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BibTeX entry: (Update)
M.A. Kupinski and M.A. Anastasio. Multiobjective Genetic Optimization of Diagnostic Classifiers with Implications for Generating Receiver Operating Characterisitic Curves. IEEE Transactions on Medical Imaging, 18(8):675--685, 1999. http://citeseer.ist.psu.edu/kupinski99multiobjective.html More
@misc{ kupinski99multiobjective,
author = "M. Kupinski and M. Anastasio",
title = "Multiobjective Genetic Optimization of Diagnostic Classifiers with Implications
for Generating Receiver Operating Characterisitic Curves",
text = "M.A. Kupinski and M.A. Anastasio. Multiobjective Genetic Optimization of
Diagnostic Classifiers with Implications for Generating Receiver Operating
Characterisitic Curves. IEEE Transactions on Medical Imaging, 18(8):675--685,
1999.",
year = "1999",
url = "citeseer.ist.psu.edu/kupinski99multiobjective.html" }
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