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 a type of “causal” (rather than “effect”) relationship, which restricts the flexibility of the algorithm to learn. In this paper, we propose ABC-Miner+, an extension to the ABC-Miner algorithm which is able to learn more flexible Bayesian network classifier structures, 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. The produced model is the Markov blanket of the class variable. Empirical evaluations on UCI benchmark datasets show that our extended ABC-Miner+ outperforms its previous version in terms of predictive accuracy, model size and computational time.
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Salama, K.M., Freitas, A.A. (2014). Extending the ABC-Miner Bayesian Classification Algorithm. In: Terrazas, G., Otero, F., Masegosa, A. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2013). Studies in Computational Intelligence, vol 512. Springer, Cham. https://doi.org/10.1007/978-3-319-01692-4_1
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DOI: https://doi.org/10.1007/978-3-319-01692-4_1
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