The Use of Vicinal-Risk Minimization for Training Decision Trees
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- @Article{Cao:2015:ASC,
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author = "Yilong Cao and Peter I. Rockett",
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title = "The Use of Vicinal-Risk Minimization for Training
Decision Trees",
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journal = "Applied Soft Computing",
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year = "2015",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2015.02.043",
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URL = "http://www.sciencedirect.com/science/article/pii/S1568494615001507",
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abstract = "We propose the use of Vapnik's vicinal risk
minimisation (VRM) for training decision trees to
approximately maximise decision margins. We implement
VRM by propagating uncertainties in the input
attributes into the labelling decisions. In this way,
we perform a global regularisation over the decision
tree structure. During a training phase, a decision
tree is constructed to minimise the total probability
of classifying the labelled training examples, a
process which approximately maximises the margins of
the resulting classifier. We perform the necessary
minimisation using an appropriate meta-heuristic
(genetic programming) and present results over a range
of synthetic and benchmark real datasets. We
demonstrate the statistical superiority of VRM training
over conventional empirical risk minimisation (ERM) and
the well-known C4.5 algorithm, for a range of synthetic
and real datasets. We also conclude that there is no
statistical difference between trees trained by ERM and
using C4.5. Training with VRM is shown to be more
stable and repeatable than by ERM.",
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keywords = "genetic algorithms, genetic programming, Decision
trees, Vicinal-risk minimisation, Decision trees,
Classification",
- }
Genetic Programming entries for
Yilong Cao
Peter I Rockett
Citations