Skip to main content

ABC-Miner+: constructing Markov blanket classifiers with ant colony algorithms

Salama, Khalid M., Freitas, Alex A. (2014) ABC-Miner+: constructing Markov blanket classifiers with ant colony algorithms. Memetic Computing, 6 (3). pp. 183-206. ISSN 1865-9284. (doi:10.1007/s12293-014-0138-6) (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/s12293-014-0138-6

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 always a type of “causal” (rather than “effect”) relationship, which restricts the flexibility of the algorithm to learn. In this paper, we extended the ABC-Miner algorithm to be able to learn the Markov blanket of the class variable. Such a produced model has a more flexible Bayesian network classifier structure, 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. In this context, we propose two algorithms: ABC-Miner+1, in which the dependency relationships between the class and the input variables are defined in a separate phase before the dependency relationships among the input variables are defined, and ABC-Miner+2, in which the two types of dependency relationships in the Markov blanket classifier are discovered in a single integrated process. Empirical evaluations on 33 UCI benchmark datasets show that our extended algorithms outperform the original version in terms of predictive accuracy, model size and computational time. Moreover, they have shown a very competitive performance against other well-known classification algorithms in the literature.

Item Type: Article
DOI/Identification number: 10.1007/s12293-014-0138-6
Uncontrolled keywords: data mining, machine learning, classification, Bayesian network classifier, 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: 15 Jun 2015 13:12 UTC
Last Modified: 29 May 2019 14:42 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/49022 (The current URI for this page, for reference purposes)
  • Depositors only (login required):