Abstract
Leave-one-out (LOO) and its generalization, K-Fold, are among most well-known cross-validation methods, which divide the sample into many folds, each one of which is, in turn, left out for testing, while the other parts are used for training. In this study, as an extension of this idea, we propose a new cross-validation approach that we called miss-one-out (MOO) that mislabels the example(s) in each fold and keeps this fold in the training set as well, rather than leaving it out as LOO does. Then, MOO tests whether the trained classifier can correct the erroneous label of the training sample. In principle, having only one fold deliberately labeled incorrectly should have only a small effect on the classifier that uses this bad-fold along with K - 1 good folds and can be utilized as a generalization measure of the classifier. Experimental results on a number of benchmark datasets and three real bioinformatics dataset show that MOO can better estimate the test set accuracy of the classifier.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Pattern Recognition and Artificial Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
