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Flexible learning of k-dependence Bayesian network classifiers

Abstract:

In this paper we present an extension to the classical kdependence Bayesian network classifier algorithm. The original method intends to work for the whole continuum of Bayesian classifiers, from na¨ıve Bayes to unrestricted networks. In our experience, it performs well for low values of k. However, the algorithm tends to degrade in more complex spaces, as it greedily tries to add k dependencies to all feature nodes of the resulting net. We try to overcome this limitation by seeking for optim...

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Publication status:
Published

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Publisher copy:
10.1145/2001576.2001741

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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Human Genetics Wt Centre
Role:
Author
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Institution:
University of Castilla-La Mancha
Role:
Author
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Grant:
Project TIN2010-20900-C04- 03
Publisher:
ACM Press Publisher's website
Host title:
Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11
Publication date:
2011-01-01
Event title:
the 13th annual conference
Event location:
Dublin,, Ireland
Event start date:
2011-07-12T00:00:00Z
Event end date:
2011-07-16T00:00:00Z
DOI:
ISBN:
9781450305570
Language:
© The authors
Keywords:
Pubs id:
1131245
Local pid:
pubs:1131245
Deposit date:
2020-09-09

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