Widened Learning of Bayesian Network Classifiers

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Sampson_0-370131.pdf(348.55 KB)
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2016
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urn:nbn:de:bsz:352-0-370131
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10.1007/978-3-319-46349-0_19
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Advances in Intelligent Data Analysis XV : 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016; Proceedings / Boström, Henrik et al. (ed.). - Cham : Springer, 2016. - (Lecture Notes in Computer Science ; 9897). - pp. 215-225. - ISSN 0302-9743. - eISSN 1611-3349. - ISBN 978-3-319-46348-3
Abstract
We demonstrate the application of Widening to learning performant Bayesian Networks for use as classifiers. Widening is a framework for utilizing parallel resources and diversity to find models in a hypothesis space that are potentially better than those of a standard greedy algorithm. This work demonstrates that widened learning of Bayesian Networks, using the Frobenius Norm of the networks’ graph Laplacian matrices as a distance measure, can create Bayesian networks that are better classifiers than those generated by popular Bayesian Network algorithms.
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004 Computer Science
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15th International Symposium, IDA 2016, Oct 13, 2016 - Oct 15, 2016, Stockholm
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ISO 690SAMPSON, Oliver R., Michael R. BERTHOLD, 2016. Widened Learning of Bayesian Network Classifiers. 15th International Symposium, IDA 2016. Stockholm, Oct 13, 2016 - Oct 15, 2016. In: BOSTRĂ–M, Henrik, ed. and others. Advances in Intelligent Data Analysis XV : 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016; Proceedings. Cham:Springer, pp. 215-225. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-46348-3. Available under: doi: 10.1007/978-3-319-46349-0_19
BibTex
@inproceedings{Sampson2016-09-21Widen-37277,
  year={2016},
  doi={10.1007/978-3-319-46349-0_19},
  title={Widened Learning of Bayesian Network Classifiers},
  number={9897},
  isbn={978-3-319-46348-3},
  issn={0302-9743},
  publisher={Springer},
  address={Cham},
  series={Lecture Notes in Computer Science},
  booktitle={Advances in Intelligent Data Analysis XV : 15th International Symposium, IDA 2016, Stockholm, Sweden, October 13-15, 2016; Proceedings},
  pages={215--225},
  editor={Boström, Henrik},
  author={Sampson, Oliver R. and Berthold, Michael R.}
}
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