Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers
Bo Jiang, Xuegong Zhang, Tianxi Cai; 9(17):521−540, 2008.
Support vector machine (SVM) is one of the most popular and promising classification algorithms. After a classification rule is constructed via the SVM, it is essential to evaluate its prediction accuracy. In this paper, we develop procedures for obtaining both point and interval estimators for the prediction error. Under mild regularity conditions, we derive the consistency and asymptotic normality of the prediction error estimators for SVM with finite-dimensional kernels. A perturbation-resampling procedure is proposed to obtain interval estimates for the prediction error in practice. With numerical studies on simulated data and a benchmark repository, we recommend the use of interval estimates centered at the cross-validated point estimates for the prediction error. Further applications of the proposed procedure in model evaluation and feature selection are illustrated with two examples.
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