Multivariate statistical simulation
Multivariate statistical simulation
Decorrelation of the true and estimated classifier errors in high-dimensional settings
EURASIP Journal on Bioinformatics and Systems Biology
Exact correlation between actual and estimated errors in discrete classification
Pattern Recognition Letters
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Analytic Study of Performance of Error Estimators for Linear Discriminant Analysis
IEEE Transactions on Signal Processing
Hi-index | 0.01 |
A recently proposed Bayesian modeling framework for classification facilitates both the analysis and optimization of error estimation performance. The Bayesian error estimator is then defined to have optimal mean-square error performance, but in many situations closed-form representations are unavailable and approximations may not be feasible. To address this, we present a method to optimally calibrate arbitrary error estimators for minimum mean-square error performance within a supposed Bayesian framework. Assuming a fixed sample size, classification rule and error estimation rule, as well as a fixed Bayesian model, the calibration is done by first computing a calibration function that maps error estimates to their optimally calibrated values off-line. Once found, this calibration function may be easily applied to error estimates on the fly whenever the assumptions apply. We demonstrate that calibrated error estimators offer significant improvement in performance relative to classical error estimators under Bayesian models with both linear and non-linear classification rules.