计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 210800172-7.doi: 10.11896/jsjkx.210800172
钟坤华1,2,3, 陈芋文1,2,3, 秦小林1,3
ZHONG Kun-hua1,2,3, CHEN Yu-wen1,2,3, QIN Xiao-lin1,3
摘要： 针对贝叶斯网络结构学习K2算法要求提供实际难以获得的准确先验节点顺序信息以及爬山算法对初始网络结构依赖性强且容易陷入局部最优的问题,提出了一种基于子网融合的贝叶斯网络结构学习算法Sub-BN-Merge。该算法首先为每个节点构造一个子网,并以Voting的方式融合生成每个节点的候选父节点集,然后基于评分函数在候选集中为每个节点搜索最优父节点集合,最后消除所得网络结构中的环路,并以此为初值进一步采用启发式搜发方法对其进行优化。在小型网络Asia、中型网络Alarm和大型网络Win95pts上进行了实验验证,同时分析了算法在数据存在缺失值情况下的性能。实验结果证明了算法的有效性,Sub-BN-Merge算法在结构汉明距和算法正确率方面优于对比算法。
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