Learning Bayesian network structures by searching for the best ordering with genetic algorithms
Learning Bayesian network structures by searching for the best ordering with genetic algorithms
复制标题
DOI：
10.1109/3468.508827
复制
发表时间：
1996-07-01
影响因子：
--
通讯作者：
Yurramendi, Y
中科院分区：
文献类型：
--
作者：
Larranaga, P;Kuijpers, CMH;Yurramendi, Y
在本文中，我们提出了一种从案例数据库中归纳贝叶斯网络结构的新方法。该方法基于通过遗传算法搜索系统变量的最佳排序。由于结束变量最优排序的问题类似于旅行商问题，因此我们使用为后一个问题开发的遗传算子。变量排序的质量通过结构学习算法 K2 进行评估。我们展示了通过模拟 ALARM 网络获得的经验结果。
In this paper we present a new methodology for inducing Bayesian network structures from a database of cases. The methodology is based on searching for the best ordering of the system variables by means of genetic algorithms. Since this problem of ending an optimal ordering of variables resembles the traveling salesman problem, we use genetic operators that were developed for the latter problem. The quality of a variable ordering is evaluated with the structure-learning algorithm K2. We present empirical results that were obtained with a simulation of the ALARM network.