Title :
Learning of Bayesian networks by a local discovery ant colony algorithm
Author :
Pinto, Pedro C. ; Nägele, Andreas ; Dejori, Mathäus ; Runkler, Thomas A. ; Sousa, João M C
Author_Institution :
Dept. of Mech. Eng., Tech. Univ. of Lisbon, Lisbon
Abstract :
Bayesian networks (BNs) are knowledge representation tools capable of representing dependence or independence relationships among random variables that compose a problem domain. Bayesian networks learned from data sets are receiving increasing attention within the community of researchers of uncertainty in artificial intelligence, due to their capacity to provide good inference models and to discover the structure of complex domains. One approach to learning BNs from data is to use a scoring metric to evaluate the fitness of any given candidate network for the database, and apply an optimization procedure to explore the set of candidate networks. Among the most frequently used optimization methods for this purpose is greedy search, either deterministic or stochastic. This article proposes a hybrid Bayesian network learning algorithm MMACO, based on the local discovery algorithm max-min parents and children (MMPC) and ant colony optimization (ACO). MMPC is used to construct the skeleton of the Bayesian network and then ACO is used to orientate its edges, thus returning the final structure. We apply MMACO (max-min ACO) to several sets of benchmark networks and show that it outperforms greedy search (GS) and simulated annealing (SA) algorithms.
Keywords :
belief networks; greedy algorithms; learning (artificial intelligence); minimax techniques; search problems; simulated annealing; artificial intelligence; greedy search; hybrid Bayesian network learning algorithm; knowledge representation; local discovery ant colony algorithm; max-min parents and children; simulated annealing; Ant colony optimization; Artificial intelligence; Bayesian methods; Databases; Inference algorithms; Knowledge representation; Learning; Optimization methods; Random variables; Uncertainty;
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
DOI :
10.1109/CEC.2008.4631166