Density Preserving Sampling: Robust and Efficient Alternative to Cross-validation for Error Estimation.

Budka, M. and Gabrys, B., 2013. Density Preserving Sampling: Robust and Efficient Alternative to Cross-validation for Error Estimation. IEEE Transactions on Neural Networks and Learning Systems, 24 (1), 22 - 34 .

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TNNLS-2012-P-0123.pdf - Accepted Version

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DOI: 10.1109/TNNLS.2012.2222925

Abstract

Estimation of the generalization ability of a classi- fication or regression model is an important issue, as it indicates the expected performance on previously unseen data and is also used for model selection. Currently used generalization error estimation procedures, such as cross-validation (CV) or bootstrap, are stochastic and, thus, require multiple repetitions in order to produce reliable results, which can be computationally expensive, if not prohibitive. The correntropy-inspired density- preserving sampling (DPS) procedure proposed in this paper eliminates the need for repeating the error estimation procedure by dividing the available data into subsets that are guaranteed to be representative of the input dataset. This allows the production of low-variance error estimates with an accuracy comparable to 10 times repeated CV at a fraction of the computations required by CV. This method can also be used for model ranking and selection. This paper derives the DPS procedure and investigates its usability and performance using a set of public benchmark datasets and standard classifiers

Item Type:Article
ISSN:1045-9227
Uncontrolled Keywords:Bootstrap, correntropy, cross-validation, error estimation, model selection, sampling
Subjects:UNSPECIFIED
Group:Faculty of Science and Technology
ID Code:20876
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:17 Jun 2013 09:08
Last Modified:10 Feb 2016 10:10

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