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Clustering-based Bayesian Multi-net Classifier Construction with Ant Colony Optimization

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)

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/10.1109/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
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: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 01 Jul 2013 17:05 UTC
Last Modified: 17 Sep 2019 09:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/34481 (The current URI for this page, for reference purposes)
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