Bayesian Receiver Operating Characteristic Metric for Linear Classifiers
This work addresses the need for computationally efficient accuracy assessment in machine learning, particularly for linear classifiers, though it appears incremental as it builds on existing Bayesian error estimation methods.
The authors tackled the problem of efficiently estimating classifier accuracy by proposing a Bayesian Area Under the ROC Curve (CBAUC) metric, which provides a closed-form solution for linear binary classifiers under Gaussian assumptions, resulting in faster and more accurate performance compared to conventional estimators as confirmed by experiments.
We propose a novel classifier accuracy metric: the Bayesian Area Under the Receiver Operating Characteristic Curve (CBAUC). The method estimates the area under the ROC curve and is related to the recently proposed Bayesian Error Estimator. The metric can assess the quality of a classifier using only the training dataset without the need for computationally expensive cross-validation. We derive a closed-form solution of the proposed accuracy metric for any linear binary classifier under the Gaussianity assumption, and study the accuracy of the proposed estimator using simulated and real-world data. These experiments confirm that the closed-form CBAUC is both faster and more accurate than conventional AUC estimators.