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Learning Bayesian network classifiers using ant colony optimization

Salama, Khalid M., Freitas, Alex A. (2013) Learning Bayesian network classifiers using ant colony optimization. Swarm Intelligence, 7 (2-3). pp. 229-254. ISSN 1935-3812. (doi:10.1007/s11721-013-0087-6) (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)

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. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/s11721-013-0087-6

Abstract

Bayesian networks are knowledge representation tools that model the (in)dependency relationships among variables for probabilistic reasoning. Classification with Bayesian networks aims to compute the class with the highest probability given a case. This special kind is referred to as Bayesian network classifiers. Since learning the Bayesian network structure from a dataset can be viewed as an optimization problem, heuristic search algorithms may be applied to build high-quality networks in medium- or large-scale problems, as exhaustive search is often feasible only for small problems. In this paper, we present our new algorithm, ABC-Miner, and propose several extensions to it. ABC-Miner uses ant colony optimization for learning the structure of Bayesian network classifiers. We report extended computational results comparing the performance of our algorithm with eight other classification algorithms, namely six variations of well-known Bayesian network classifiers, cAnt-Miner for discovering classification rules and a support vector machine algorithm.

Item Type: Article
DOI/Identification number: 10.1007/s11721-013-0087-6
Uncontrolled keywords: data mining, classification, ant colony optimization, swarm intelligence, Bayesian network classifiers
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 06 Dec 2013 17:57 UTC
Last Modified: 29 May 2019 11:35 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/37253 (The current URI for this page, for reference purposes)
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