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Extending the ABC-Miner Bayesian classification algorithm.

Salama, Khalid M. and Freitas, Alex A. (2013) Extending the ABC-Miner Bayesian classification algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2013) Learning, Optimization and Interdisciplinary Applications. Studies in Computational Intelligence . Springer, Cham, Switzerland, pp. 1-12. ISBN 978-3-319-01691-7. E-ISBN 978-3-319-01692-4. (doi:10.1007/978-3-319-01692-4_1) (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)

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Official URL
http://dx.doi.org/10.1007/978-3-319-01692-4_1

Abstract

ABC-Miner is a Bayesian classification algorithm based on the Ant Colony Optimization (ACO) meta-heuristic. The algorithm learns Bayesian network Augmented Naïve-Bayes (BAN) classifiers, where the class node is the parent of all the nodes representing the input variables. However, this assumes the existence of a dependency relationship between the class variable and all the input variables, and this relationship is a type of “causal” (rather than “effect”) relationship, which restricts the flexibility of the algorithm to learn. In this paper, we propose ABC-Miner+, an extension to the ABC-Miner algorithm which is able to learn more flexible Bayesian network classifier structures, where it is not necessary to have a (direct) dependency relationship between the class variable and each of the input variables, and the dependency between the class and the input variables varies from “causal” to “effect” relationships. The produced model is the Markov blanket of the class variable. Empirical evaluations on UCI benchmark datasets show that our extended ABC-Miner+ outperforms its previous version in terms of predictive accuracy, model size and computational time.

Item Type: Book section
DOI/Identification number: 10.1007/978-3-319-01692-4_1
Uncontrolled keywords: data mining, machine learning, ant colony optimization, 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: 24 Oct 2013 17:04 UTC
Last Modified: 14 Oct 2019 10:52 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/35628 (The current URI for this page, for reference purposes)
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