Salama, K.M. and Freitas, A.A. (2012) ABC-Miner: an ant-based Bayesian classification algorithm. In: Swarm Intelligence: 8th International Conference (ANTS 2012). , September, 2012, Brussels, Belgium.
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Bayesian networks (BNs) are powerful tools for knowledge representation and inference that encode (in)dependencies among random variables. A Bayesian network classifier is a special kind of these networks that aims to compute the posterior probability of each class given an instance of the attributes and predicts the class with the highest posterior probability. Since learning the optimal BN structure from a dataset is NP -hard, heuristic search algorithms need to be applied effectively to build high-quality networks. In this paper, we propose a novel algorithm, called ABC-Miner, for learning the structure of BN classifiers using the Ant Colony Optimization (ACO) meta-heuristic. We describe all the elements necessary to tackle our learning problem using ACO, and experimentally compare the performance of our ant-based Bayesian classification algorithm with other algorithms for learning BN classifiers used in the literature.
|Item Type:||Conference or workshop item (Paper)|
|Uncontrolled keywords:||data mining, ant colony optimization|
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
|Divisions:||Faculties > Science Technology and Medical Studies > School of Computing > Computational Intelligence Group|
|Depositing User:||Alex Freitas|
|Date Deposited:||14 Nov 2012 14:17|
|Last Modified:||09 Apr 2013 08:54|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/32165 (The current URI for this page, for reference purposes)|
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