Receiver operating characteristic curves and confidence bands for support vector machines.

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ID: 113640
2020
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Abstract
Many problems that appear in biomedical decision making, such as diagnosing disease and predicting response to treatment, can be expressed as binary classification problems. The support vector machine (SVM) is a popular classification technique that is robust to model misspecification and effectively handles high-dimensional data. The relative costs of false positives and false negatives can vary across application domains. The receiving operating characteristic (ROC) curve provides a visual representation of the trade-off between these two types of errors. Because the SVM does not produce a predicted probability, an ROC curve cannot be constructed in the traditional way of thresholding a predicted probability. However, a sequence of weighted SVMs can be used to construct an ROC curve. While ROC curves constructed using weighted SVMs have great potential for allowing ROC curves analyses that cannot be done by thresholding predicted probabilities, their theoretical properties have heretofore been underdeveloped. We propose a method for constructing confidence bands for the SVM ROC curve and provide the theoretical justification for the SVM ROC curve by showing that the risk function of the estimated decision rule is uniformly consistent across the weight parameter. We demonstrate the proposed confidence band method using simulation studies. We present a predictive model for treatment response in breast cancer as an illustrative example.
Reference Key
luckett2020receiverbiometrics Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Luckett, Daniel J;Laber, Eric B;El-Kamary, Samer S;Fan, Cheng;Jhaveri, Ravi;Perou, Charles M;Shebl, Fatma M;Kosorok, Michael R;
Journal biometrics
Year 2020
DOI
10.1111/biom.13365
URL
Keywords
Machine learning classification diagnostic medicine outcome weighted learning

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