计算机科学 ›› 2023, Vol. 50 ›› Issue (11A): 220800046-7.doi: 10.11896/jsjkx.220800046
李晓晴, 于海征
LI Xiaoqing, YU Haizheng
摘要： 贝叶斯网络源于对人工智能领域不确定问题的研究,是进行不确定问题推理和数据分析的重要工具。自贝叶斯网络结构学习诞生以来,已有众多成熟的结构学习算法,包括基于依赖分析的方法、基于评分搜索的方法和混合搜索的方法。其中利用信息论进行结构修剪已成为常用手段,但条件互信息中条件集的选取并没有统一标准,导致网络结构的修剪不一致。爬山算法利用3种搜索算子对网络结构进行局部更新,通过评分函数得到最优结构。结合信息论和爬山算法思想,提出一种新的结构学习算法——CMIHC(Conditional Mutual Information Hill Climbing)算法。该算法利用互信息和创建的条件集修剪初始连通图,对其进行定向,进而得到初始网络结构,结合评分函数和爬山算法的贪婪搜索策略得到最优网络结构。通过实验分析,在精度和效率上,CMIHC算法效果优于其他对比算法。
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