Salama, Khalid M. and Freitas, Alex A.
(2012)
*
ABC-Miner: an ant-based Bayesian classification algorithm.
*
In:
Swarm Intelligence 8th International Conference.
Lecture Notes in Computer Science
.
Springer, Berlin, Germany, pp. 13-24.
ISBN 978-3-642-32649-3.
E-ISBN 978-3-642-32650-9.
(doi:10.1007/978-3-642-32650-9_2)
(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:32165)

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/10.1007/978-3-642-32650-9_2 |

## Abstract

Bayesian networks (BNs) are powerful tools for knowledge representation and inference that encode (in)dependencies among random variables. A Bayesian network classifier is a special kind of these networks that aims to compute the posterior probability of each class given an instance of the attributes and predicts the class with the highest posterior probability. Since learning the optimal BN structure from a dataset is NP -hard, heuristic search algorithms need to be applied effectively to build high-quality networks. In this paper, we propose a novel algorithm, called ABC-Miner, for learning the structure of BN classifiers using the Ant Colony Optimization (ACO) meta-heuristic. We describe all the elements necessary to tackle our learning problem using ACO, and experimentally compare the performance of our ant-based Bayesian classification algorithm with other algorithms for learning BN classifiers used in the literature.

Item Type: | Book section |
---|---|

DOI/Identification number: | 10.1007/978-3-642-32650-9_2 |

Uncontrolled keywords: | data mining, ant colony optimization |

Subjects: |
Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems) |

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

Depositing User: | Alex Freitas |

Date Deposited: | 14 Nov 2012 14:17 UTC |

Last Modified: | 16 Nov 2021 10:09 UTC |

Resource URI: | https://kar.kent.ac.uk/id/eprint/32165 (The current URI for this page, for reference purposes) |

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

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