Skip to main content

Classification with cluster-based Bayesian multi-nets using Ant Colony Optimisation

Salama, Khalid M., Freitas, Alex A. (2014) Classification with cluster-based Bayesian multi-nets using Ant Colony Optimisation. Swarm and Evolutionary Computation, 18 . pp. 54-70. ISSN 2210-6502. (doi:10.1016/j.swevo.2014.05.001) (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://www.dx.doi.org/10.1016/j.swevo.2014.05.001

Abstract

Bayesian multi-net (BMN) classifiers consist of several local models, one for each data subset, to model asymmetric, more consistent dependency relationships among variables in each subset. This paper extends an earlier work of ours and proposes several contributions to the field of clustering-based BMN classifiers, using Ant Colony Optimisation (ACO). First, we introduce a new medoid-based method for ACO-based clustering in the Ant-ClustBMB algorithm to learn BMNs. Both this algorithm and our previously introduced Ant-ClustBIB for instance-based clustering have their effectiveness empirically compared in the context of the “cluster-then-learn” approach, in which the ACO clustering step completes before learning the local BN classifiers. Second, we propose a novel “cluster-with-learn” approach, in which the ACO meta-heuristic performs the clustering and the BMN learning in a synergistic fashion. Third, we adopt the latter approach in two new ACO algorithms: ACO-ClustBIB, using the instance-based method, and ACO-ClustBMB, using the medoid-based method. Empirical results are obtained on 30 UCI datasets.

Item Type: Article
DOI/Identification number: 10.1016/j.swevo.2014.05.001
Uncontrolled keywords: Data mining; Ant Colony Optimisation; Bayesian network classifiers; Cluster-based Bayesian multi-nets
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 10 Aug 2015 17:00 UTC
Last Modified: 29 May 2019 15:54 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50098 (The current URI for this page, for reference purposes)
  • Depositors only (login required):