Receiver operating characteristic curves and confidence bands for support vector machines.
Biometrics
; 77(4): 1422-1430, 2021 12.
Article
en En
| MEDLINE
| ID: mdl-32865820
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. Although 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.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias de la Mama
/
Máquina de Vectores de Soporte
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Female
/
Humans
Idioma:
En
Revista:
Biometrics
Año:
2021
Tipo del documento:
Article