Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
Strategies for improving the modeling and interpretability of Bayesian networks
Data & Knowledge Engineering
Journal of Biomedical Informatics
Structure Learning of Bayesian Networks Using Dual Genetic Algorithm
IEICE - Transactions on Information and Systems
Algorithm for graphical Bayesian modeling based on multiple regressions
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
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Abductive inference in Bayesian belief networks (BBNs) is intended as the process of generating the K most probable configurations given observed evidence. When we are interested only in a subset of the network's variables, this problem is called partial abductive inference. Both problems are NP-hard, and so exact computation is not always possible. In this paper, a genetic algorithm is used to perform partial abductive inference in BBNs. The main contribution is the introduction of new genetic operators designed specifically for this problem. By using these genetic operators, we try to take advantage of the calculations previously carried out, when a new individual is evaluated. The algorithm is tested using a widely-used Bayesian network and a randomly generated one, and then compared with a previous genetic algorithm based on classical genetic operators. From the experimental results, we conclude that the new genetic operators preserve the accuracy of the previous algorithm and also reduce the number of operations performed during the evaluation of individuals. The performance of the genetic algorithm is thus improved