Ni, J. and Rockett, P. (2015) Training genetic programming classifiers by vicinal-risk minimization. Genetic Programming and Evolvable Machines, 16 (1). 3 - 25. ISSN 1389-2576
Abstract
We propose and motivate the use of vicinal-risk minimization (VRM) for training genetic programming classifiers. We demonstrate that VRM has a number of attractive properties and demonstrate that it has a better correlation with generalization error compared to empirical risk minimization (ERM) so is more likely to lead to better generalization performance, in general. From the results of statistical tests over a range of real and synthetic datasets, we further demonstrate that VRM yields consistently superior generalization errors compared to conventional ERM.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014, Springer Science+Business Media New York. This is an author produced version of a paper subsequently published in Genetic Programming and Evolvable Machines. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Genetic programming; Classification; Vicinal-risk minimization |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Nov 2015 17:10 |
Last Modified: | 21 Mar 2018 18:21 |
Published Version: | https://dx.doi.org/10.1007/s10710-014-9222-4 |
Status: | Published |
Publisher: | Springer Verlag |
Refereed: | Yes |
Identification Number: | 10.1007/s10710-014-9222-4 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:90871 |