Abstract
Accordering to the hybrid Bayesian networks learning algorithms which are easy to narrow the search space and fall into local optimum, a Bayesian network learning algorithm based on ant colony optimization is proposed. Firstly, this paper applies max-min parents and children (MMPC) to construct the framework of the undirected network, and then uses ant colony optimization to score-search, by balancing the "exploitation" and "exploration" to repair the search space and determine the direction of edges in the network. Finally applying the algorithm to learn a logical alarm reduction mechanism (ALARM) network shows that it reduces the number of missing edges, and gets closer to the real structure of Bayesian network.
Original language | English |
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Pages (from-to) | 1509-1512 |
Number of pages | 4 |
Journal | Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics |
Volume | 32 |
Issue number | 7 |
DOIs | |
State | Published - Jul 2010 |
Keywords
- Ant colony optimization
- Bayesian networks
- Structure learning