一种通过结构边界进行贝叶斯网络学习的算法
doi: 10.11999/JEIT140786
Learning Bayesian Network from Structure Boundaries
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摘要: 贝叶斯网络是智能算法领域重要的理论工具,其结构学习问题被认为是NP-hard问题。该文通过混合学习算法的方式,从分析低阶条件独立性测试提供的信息入手,给出了构造目标网络结构空间边界的方法,并给出了完整的证明。在此基础上执行打分搜索算法获得最终的网络结构。仿真结果表明该算法与同类算法相比具有更高的精度和更好的执行效率。Abstract: Bayesian network is an important theoretical tool in the artificial algorithm field, and learning structure from data is considered as NP-hard. In this article, a hybrid learning method is proposed by starting from analysis of information provided by low-order conditional independence testing. The methods of constructing boundaries of the structure space of the target network are given, as well as the complete theoretical proof. A search scoring algorithm is operated to find the final structure of the network. Simulation results show that the hybrid learning method proposed in this article has higher learning precision and is more efficient than similar algorithms.
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