An Efficient Data Partitioning to Improve Classification Performance While Keeping Parameters Interpretable.

ABSTRACT: Supervised machine learning methods typically require splitting data into multiple chunks for training, validating, and finally testing classifiers. For finding the best parameters of a classifier, training and validation are usually carried out with cross-validation. This is followed by application of the classifier with optimized parameters to a separate test set for estimating the classifier's generalization performance. With limited data, this separation of test data creates a difficult trade-off between having more statistical power in estimating generalization performance versus choosing better parameters and fitting a better model. We propose a novel approach that we term "Cross-validation and cross-testing" improving this trade-off by re-using test data without biasing classifier p

SUBMITTER: Korjus K 

PROVIDER: S-EPMC5001642 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature