摘要
贝叶斯网络是不确定性环境下知识表示和推理的有效工具之一。现有的贝叶斯网络结构学习算法不同程度地存在学习效率偏低的问题,为此,本文提出一种高效而且可靠的贝叶斯网络结构学习算法ISOR。首先使用最大生成树算法和启发式切割集搜索算法以确定网络中所有可能的边,然后结合碰撞识别方法和启发式打分-搜索方法识别出所有边的方向,最后进行冗余边检验。与当前基于依赖分析的其它算法相比,该算法有效降低条件独立性检验的次数和阶数。算法分析和应用于Alarm网络的实验结果均表明,算法ISOR具有良好的性能。
Bayesian network is a powerful knowledge representation and reasoning tool under uncertain conditions . Current algorithms for learning Bayesian networks structures are inefficient to a certain degree. Therefore,an efficient and reliable algorithm, ISOR, is proposed in this paper. Firstly, all the potential edges of the underlying network are produced by the maximum weight spanning tree algorithm and heuristic cut-set searching algorithm. Then, methods based on identifying colliders and scoring-search methods are integrated to orient all the edges in the network . Finally , redundant edges in the network are removed . Compared with other current algorithms based on dependency analysis, the proposed algorithm greatly reduces the number and the order of conditional independence tests . Algorithm analysis and experimental results on Alarm network show algorithm ISOR has good performance.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2006年第4期445-449,共5页
Pattern Recognition and Artificial Intelligence
基金
安徽省自然科学基金(No.050420207)
关键词
贝叶斯网络
结构学习
依赖分析
打分-搜索
Bayesian Networks, Structure Learning, Dependency Analysis, Scoring-Search
