Training genetic programming classifiers by vicinal-risk minimization

Ni, Ji and Rockett, Peter (2015) Training genetic programming classifiers by vicinal-risk minimization. Genetic Programming and Evolvable Machine, 16 (1). pp. 3-25. ISSN 1389-2576


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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.

Keywords:Genetic programming, Classification, Vicinal-risk minimization, JCNotOpen
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G340 Statistical Modelling
Divisions:College of Science > School of Computer Science
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ID Code:19206
Deposited On:23 Oct 2015 14:34

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