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Ant colony algorithms for constructing Bayesian multi-net classifiers

Salama, Khalid M., Freitas, Alex A. (2015) Ant colony algorithms for constructing Bayesian multi-net classifiers. Intelligent Data Analysis, 19 (2). pp. 233-257. ISSN 1088-467X. (doi:10.3233/IDA-150715) (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/10.3233/IDA-150715

Abstract

Bayesian Multi-nets (BMNs) are a special kind of Bayesian network (BN) classifiers that consist of several local Bayesian networks, one for each predictable class, to model an asymmetric set of variable dependencies given each class value. Deterministic methods using greedy local search are the most frequently used methods for learning the structure of BMNs based on optimizing a scoring function. Ant Colony Optimization (ACO) is a meta-heuristic global search method for solving combinatorial optimization problems, inspired by the behavior of real ant colonies. In this paper, we propose two novel ACO-based algorithms with two different approaches to build BMN classifiers: ABC-Miner^{mn}_l and ABC-Miner^{mn}_g . The former uses a local learning approach, in which the ACO algorithm completes the construction of one local BN at a time. The latter uses a global approach, which involves building a complete BMN classifier by each single ant in the colony. We experimentally evaluate the performance of our ant-based algorithms on 33 benchmark classification datasets, where our proposed algorithms are shown to be significantly better than other commonly used deterministic algorithms for learning various Bayesian classifiers in the literature, as well as competitive to other well-known classification algorithms.

Item Type: Article
DOI/Identification number: 10.3233/IDA-150715
Uncontrolled keywords: data mining, machine learning, classification, Bayesian network classifiers, ant colony optimization
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 12 Aug 2015 08:41 UTC
Last Modified: 29 May 2019 15:55 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50174 (The current URI for this page, for reference purposes)
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