poster

Ensembling Bayesian network structure learning on limited data

Authors:

Feng Liu,

Fengzhan Tian,

Qiliang ZhuAuthors Info & Claims

CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management

Pages 927 - 930

https://doi.org/10.1145/1321440.1321577

Published: 06 November 2007 Publication History

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Abstract

In recent years, Bagging method has been applied to learn Bayesian networks (BNs), especially on limited datasets. However, the BNs learned using Bagging method from limited datasets can be biased towards complex models. We present an efficient approach to produce more accurate BNs from limited datasets. Based on the Markov condition of BN learning, we proposed a novel sampling method, called Root Nodes based Sampling (RNS), and a BNs fusion method. The experimental results reveal that our ensemble method can achieve more accurate results in terms of accuracy on limited datasets.

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Cited By

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Duangsoithong RPhukpattaranont PWindeatt T(2013)Bootstrap Causal Feature Selection for irrelevant feature eliminationThe 6th 2013 Biomedical Engineering International Conference10.1109/BMEiCon.2013.6687638(1-5)Online publication date: Oct-2013
https://doi.org/10.1109/BMEiCon.2013.6687638
Hellman SMcGovern AXue M(2012)Learning ensembles of Continuous Bayesian Networks: An application to rainfall prediction2012 Conference on Intelligent Data Understanding10.1109/CIDU.2012.6382191(112-117)Online publication date: Oct-2012
https://doi.org/10.1109/CIDU.2012.6382191

Index Terms

Ensembling Bayesian network structure learning on limited data
1. Computing methodologies
  1. Machine learning
    1. Learning settings
2. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management

November 2007

1048 pages

ISBN:9781595938039

DOI:10.1145/1321440

Co-chair:
Alberto H. F. Laender,
Conference Chairs:
André O. Falcão
Universidade de Lisboa, Portugal
,
Øystein Haug Olsen,
General Chair:
Mário J. Silva
(Universidade de Lisboa, Portugal)
,
Program Chairs:
Ricardo Baeza-Yates,
Deborah L. McGuinness,
Bjorn Olstad

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2007

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CIKM07

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CIKM07: Conference on Information and Knowledge Management

November 6 - 10, 2007

Lisbon, Portugal

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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Duangsoithong RPhukpattaranont PWindeatt T(2013)Bootstrap Causal Feature Selection for irrelevant feature eliminationThe 6th 2013 Biomedical Engineering International Conference10.1109/BMEiCon.2013.6687638(1-5)Online publication date: Oct-2013
https://doi.org/10.1109/BMEiCon.2013.6687638
Hellman SMcGovern AXue M(2012)Learning ensembles of Continuous Bayesian Networks: An application to rainfall prediction2012 Conference on Intelligent Data Understanding10.1109/CIDU.2012.6382191(112-117)Online publication date: Oct-2012
https://doi.org/10.1109/CIDU.2012.6382191

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Discrete Bayesian Network Classifiers: A Survey

Histogram distance-based Bayesian Network structure learning: A supervised classification specific approach

Different Bayesian Network Models in the Classification of Remote Sensing Images