Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/54731
Type of publication: research article
Type of publication (PDB): Straipsnis Clarivate Analytics Web of Science / Article in Clarivate Analytics Web of Science (S1)
Field of Science: Informatika / Informatics (N009)
Author(s): Gvardinskas, Mindaugas;Tamošiūnaitė, Minija
Title: Approximation of unbiased convex classification error rate estimator
Is part of: Informacinės technologijos ir valdymas = Information technology and control. Kaunas : Technologija, 2016, t. 45, nr. 2
Extent: p. 148-155
Date: 2016
Keywords: Error estimation;Resubstitution;Cross-validation;Bootstrap
Abstract: Convex classification error rate estimator is described as weighted combination of the low-biased estimator and the high-biased estimator. If the underlying data model is known, the coefficients (weights) can be optimized so that the bias and root-mean-square error of the estimator is minimized. However, in most situations, data model is unknown. In this paper we propose a new error estimation method, based on approximation of unbiased convex error rate estimator. Experiments with real world and synthetic data sets show that common error estimation methods, such as resubstitution, repeated 10-foldcross-validation, leave-one-out and random subsampling are outperformed (in terms of root-mean-square error) by the proposed method
Internet: https://doi.org/10.5755/j01.itc.45.2.12052
Affiliation(s): Matematikos ir statistikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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