Bounding the asymmetric error of a convex combination of classifiers.
Author affiliations
DOI:
https://doi.org/10.15625/1813-9663/28/4/2363Keywords:
asymmetric error, asymmetric boosting, imbalanced classification, Rademacher complexityAbstract
Asymmetric error is an error that trades off between the false positive rate and the false negative rate of a binary classifier. It has been recently used in solving the imbalanced classification problem e.g., in asymmetric boosting. However, to date, the relationship between an empirical asymmetric error and its generalization counterpart has not been addressed. Bounds on the classical generalization error are not directly applicable since different penalties are associated with the false positive rate and the false negative rate respectively, and the class probability is typically ignored in the training set. In this paper, we present a bound on the expected asymmetric error of any convex combination of classifiers based on its empirical asymmetric error. We also show that the bound is a generalization of one of the latest (and tightest) bounds on the classification error of the combined classifier.
Metrics
Downloads
Published
How to Cite
Issue
Section
License
1. We hereby assign copyright of our article (the Work) in all forms of media, whether now known or hereafter developed, to the Journal of Computer Science and Cybernetics. We understand that the Journal of Computer Science and Cybernetics will act on my/our behalf to publish, reproduce, distribute and transmit the Work.2. This assignment of copyright to the Journal of Computer Science and Cybernetics is done so on the understanding that permission from the Journal of Computer Science and Cybernetics is not required for me/us to reproduce, republish or distribute copies of the Work in whole or in part. We will ensure that all such copies carry a notice of copyright ownership and reference to the original journal publication.
3. We warrant that the Work is our results and has not been published before in its current or a substantially similar form and is not under consideration for another publication, does not contain any unlawful statements and does not infringe any existing copyright.
4. We also warrant that We have obtained the necessary permission from the copyright holder/s to reproduce in the article any materials including tables, diagrams or photographs not owned by me/us.
