Salama, Khalid M. and Freitas, Alex A.
(2013)
*
Clustering-based Bayesian Multi-net Classifier Construction with Ant Colony Optimization.
*
In:
2013 IEEE Congress on Evolutionary Computation.
IEEE, pp. 3079-3086.
ISBN 978-1-4799-0453-2.
E-ISBN 978-1-4799-0454-9.
(doi:10.1109/CEC.2013.6557945)
(The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)
(KAR id:34481)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |

Official URL http://dx.doi.org/10.1109/CEC.2013.6557945 |

## Abstract

Bayesian Multi-nets (BMNs) are a special kind of Bayesian network (BN) classifiers that consist of several local networks, typically, one for each predictable class, to model an asymmetric set of variable dependencies given each class value. Alternatively, multi-nets can be learnt upon arbitrary partitions of a dataset, in which each partition holds more consistent variable dependencies given the data subset in the partition. This paper proposes two contributions to the approach that clusters the dataset into separate data subsets to build asymmetric local BN classifiers, one for each subset. First, we extend the K-modes algorithm, previously used by the Case-Based Bayesian Network Classifiers (CBBN) approach to create clusters before learning the BN classifiers. Second, we introduce the Ant-Clust-B algorithm that employs Ant Colony Optimization (ACO) to learn clustering-based BMNs. Ant-Clust-B uses ACO in the clustering step before learning the local BN classifiers. Empirical results are obtained from experiments on 18 UCI datasets.

Item Type: | Book section |
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DOI/Identification number: | 10.1109/CEC.2013.6557945 |

Uncontrolled keywords: | ant colony optimization, classificaiton, clustering, data mining, machine learning, Bayesian network classifier |

Subjects: | Q Science > Q Science (General) > Q335 Artificial intelligence |

Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |

Depositing User: | Alex Freitas |

Date Deposited: | 01 Jul 2013 17:05 UTC |

Last Modified: | 16 Nov 2021 10:11 UTC |

Resource URI: | https://kar.kent.ac.uk/id/eprint/34481 (The current URI for this page, for reference purposes) |

Freitas, Alex A.: | https://orcid.org/0000-0001-9825-4700 |

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