Classification of gene-expression data: The manifold-based metric learning way
Pattern Recognition
Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers
The Journal of Machine Learning Research
Artificial Neural Network to Predict Skeletal Metastasis in Patients with Prostate Cancer
Journal of Medical Systems
Conditional confidence intervals for classification error rate
Computational Statistics & Data Analysis
Probabilities of discrepancy between minima of cross-validation, Vapnik bounds and true risks
International Journal of Applied Mathematics and Computer Science
KDLL'06 Proceedings of the 2006 international conference on Knowledge Discovery in Life Science Literature
A new monte carlo-based error rate estimator
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
Resampling methods for meta-model validation with recommendations for evolutionary computation
Evolutionary Computation
Hi-index | 3.84 |
Motivation: Estimation of misclassification error has received increasing attention in clinical diagnosis and bioinformatics studies, especially in small sample studies with microarray data. Current error estimation methods are not satisfactory because they either have large variability (such as leave-one-out cross-validation) or large bias (such as resubstitution and leave-one-out bootstrap). While small sample size remains one of the key features of costly clinical investigations or of microarray studies that have limited resources in funding, time and tissue materials, accurate and easy-to-implement error estimation methods for small samples are desirable and will be beneficial. Results: A bootstrap cross-validation method is studied. It achieves accurate error estimation through a simple procedure with bootstrap resampling and only costs computer CPU time. Simulation studies and applications to microarray data demonstrate that it performs consistently better than its competitors. This method possesses several attractive properties: (1) it is implemented through a simple procedure; (2) it performs well for small samples with sample size, as small as 16; (3) it is not restricted to any particular classification rules and thus applies to many parametric or non-parametric methods. Contact: wfu@stat.tamu.edu