Abstract
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 \({\cal NP}\)-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.
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References
Buntine, W.: Theory refinement on Bayesian networks. In: 17th Conference on Uncertainty in Artificial Intelligence, pp. 52–60. Morgan Kaufmann (1991)
De Campos, L.M., Gámez, J.A., Puerta, J.M.: Learning Bayesian network by ant colony optimisation. Mathware and Soft Computing, 251–268 (2002)
Cheng, J., Greiner, R.: Comparing Bayesian network classifiers. In: 15th Annual Conference on Uncertainty in Artificial Intelligence, pp. 101–108 (1999)
Cheng, J., Greiner, R.: Learning Bayesian Belief Network Classifiers: Algorithms and System. In: 14th Biennial Conference: Advances in Artificial Intelligence, pp. 141–151 (2001)
Chickering, D., Geiger, M., Heckerman, D.: Learning Bayesian networks is NP-complete. Advanced Technologies Division, Microsoft Corporation, Redmond, WA, Technical Report (1994)
Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning Journal, 309–348 (1992)
Daly, R., Shen, Q., Aitken, S.: Using ant colony optimization in learning Bayesian network equivalence classes. In: Proceedings of UKCI, pp. 111–118 (2006)
Daly, R., Shen, Q.: Learning Bayesian network equivalence classes with ant colony optimization. Journal of Artificial Intelligence Research, 391–447 (2009)
Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press (2004)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian Network Classifiers. Machine Learning Journal, 131–161 (1997)
Friedman, N., Goldszmidt, M.: Learning Bayesian networks with local structure. Learning in Graphical Models, pp. 421–460. Kluwer, Norwell (1998)
Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning Journal, 197–244 (1995)
Martens, D., Backer, M.D., Haesen, R., Vanthienen, J., Snoeck, M., Baesens, B.: Classification with ant colony optimization. In: IEEE TEC, pp. 651–665 (2007)
Otero, F.E.B., Freitas, A.A., Johnson, C.G.: cAnt-Miner: An Ant Colony Classification Algorithm to Cope with Continuous Attributes. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 48–59. Springer, Heidelberg (2008)
Parpinelli, R.S., Lopes, H.S., Freitas, A.: Data mining with an ant colony optimization algorithm. In: IEEE TEC, pp. 321–332 (2002)
Pinto, P.C., Nägele, A., Dejori, M., Runkler, T.A., Costa, J.M.: Learning of Bayesian networks by a local discovery ant colony algorithm. In: IEEE World Congress on Computational Intelligence, pp. 2741–2748 (2008)
Pinto, P.C., Nägele, A., Dejori, M., Runkler, T.A., Costa, J.M.: Using a Local Discovery Ant Algorithm for Bayesian Network Structure Learning. In: IEEE TEC, pp. 767–779 (2009)
Salama, K.M., Abdelbar, A.M., Freitas, A.A.: Multiple pheromone types and other extensions to the Ant-Miner classification rule discovery algorithm. Swarm Intelligence Journal, 149–182 (2011)
UCI Repository of Machine Learning Databases, http://archive.ics.uci.edu/ml/index.html (retrieved October 2011)
Witten, H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kauffman (2005)
Yanghui, W., McCall, J., Corne, D.: Two novel Ant Colony Optimization approaches for Bayesian network structure learning. In: IEEE World Congress on Evolutionary Computation, pp. 1–7 (2010)
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Salama, K.M., Freitas, A.A. (2012). ABC-Miner: An Ant-Based Bayesian Classification Algorithm. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2012. Lecture Notes in Computer Science, vol 7461. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32650-9_2
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DOI: https://doi.org/10.1007/978-3-642-32650-9_2
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