Abstract
Bayesian networks are a powerful approach for representing and reasoning under conditions of uncertainty. Many researchers aim to find good algorithms for learning Bayesian networks from data. And the bio-inspired search algorithm is one of the most effective algorithms. We proposed a hybrid algorithm called MIC-BPSO (Maximal Information Coefficient – Binary Particle Swarm Optimization). This algorithm firstly applies network construction method based on Maximal Information Coefficient to improve the quality of initial particles, and then uses the decomposability of scoring function to modify BPSO algorithm. Experiment results show that, without a given node ordering, this algorithm outperforms MI-BPSO, I-BN-PSO, MWST-HC and K2 algorithm.
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Li, G., Xing, L., Chen, Y. (2015). A New BN Structure Learning Mechanism Based on Decomposability of Scoring Functions. In: Gong, M., Linqiang, P., Tao, S., Tang, K., Zhang, X. (eds) Bio-Inspired Computing -- Theories and Applications. BIC-TA 2015. Communications in Computer and Information Science, vol 562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49014-3_19
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DOI: https://doi.org/10.1007/978-3-662-49014-3_19
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