Conference item
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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Bibliographic Details
- 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:
- 2011-07-12
- Event end date:
- 2011-07-16
- DOI:
- ISBN:
- 9781450305570
Item Description
- Language:
- © The authors
- Keywords:
- Pubs id:
-
1131245
- Local pid:
- pubs:1131245
- Deposit date:
- 2020-09-09
Terms of use
- Copyright holder:
- © The Authors
- Copyright date:
- 2011
- Rights statement:
- This is the version of record of the conference item. The final version is available online from ACM at: https://doi.org/10.1145/2001576.2001741
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