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Citation
Marco Barreno, Alvaro Cardenas, Doug Tygar. "Optimal ROC Curve for a Combination of Classifiers.". Advances in Neural Information Processing Systems (NIPS), MIT Press, pp. 57-64, 2008.

Abstract
We present a new analysis for the combination of binary classifiers. Our analysis makes use of the Neyman-Pearson lemma as a theoretical basis to analyze combinations of classifiers. We give a method for finding the optimal decision rule for a combination of classifiers and prove that it has the optimal ROC curve. We show how our method generalizes and improves previous work on combining classifiers and generating ROC curves.

Electronic downloads

• Optimal_ROC_curve.pdf · application/pdf · 305 kbytes

• HTML

  Marco Barreno, Alvaro Cardenas, Doug Tygar. <a
  href="http://www.truststc.org/pubs/748.html"
  >Optimal ROC Curve for a Combination   of
  Classifiers.</a>, <i>Advances in Neural
  Information Processing Systems (NIPS), MIT Press</i>,
  pp. 57-64,  2008.

• Plain text

  Marco Barreno, Alvaro Cardenas, Doug Tygar. "Optimal
  ROC Curve for a Combination   of Classifiers.".
  <i>Advances in Neural Information Processing Systems
  (NIPS), MIT Press</i>, pp. 57-64,  2008.

• BibTeX

  @article{BarrenoCardenasTygar08_OptimalROCCurveForCombinationOfClassifiers,
      author = {Marco Barreno and Alvaro Cardenas and Doug Tygar},
      title = {Optimal ROC Curve for a Combination   of
                Classifiers.},
      journal = {Advances in Neural Information Processing Systems
                (NIPS), MIT Press},
      pages = {57-64},
      year = {2008},
      abstract = {We present a new analysis for the combination of
                binary classifiers. Our analysis makes use of the
                Neyman-Pearson lemma as a theoretical basis to
                analyze combinations of classifiers. We give a
                method for finding the optimal decision rule for a
                combination of classifiers and prove that it has
                the optimal ROC curve. We show how our method
                generalizes and improves previous work on
                combining classifiers and generating ROC curves.},
      URL = {http://www.truststc.org/pubs/748.html}
  }
  

Posted by Jessica Gamble on 4 May 2010.
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