基于超结构的BN随机搜索学习算法
发布时间：2019-05-07 01:55
【摘要】：近年来,贝叶斯网络(Bayesian network,BN)在不确定性知识表示与概率推理方面发挥着越来越重要的作用.其中,BN结构学习是BN推理中的重要问题.然而,在当前BN结构的2阶段混合学习算法中,大多存在一些问题:第1阶段无向超结构学习中存在容易丢失弱关系的边的问题;第2阶段的爬山搜索算法存在易陷入局部最优的问题.针对这2个问题,首先采用Opt01ss算法学习超结构,尽可能地避免出现丢边现象;然后给出基于超结构的搜索算子,分析初始网络的随机选择规则和对初始网络随机优化策略,重点提出基于超结构的随机搜索的SSRandom结构学习算法,该算法一定程度上可以很好地跳出局部最优极值;最后在标准Survey,Asia,Sachs网络上,通过灵敏性、特效性、欧几里德距离和整体准确率4个评价指标,并与已有3种混合学习算法的实验对比分析,验证了该学习算法的良好性能.
[Abstract]:In recent years, Bayesian networks (Bayesian network,BN) play an increasingly important role in uncertain knowledge representation and probabilistic reasoning. Among them, BN structure learning is an important problem in BN reasoning. However, in the current two-stage hybrid learning algorithms of BN structure, there are some problems: the first stage of undirected superstructure learning has the problem of easily losing the weak relations; The second stage of mountain climbing search algorithm is prone to fall into the local optimal problem. In order to solve these two problems, firstly, Opt01ss algorithm is used to study the superstructure, so as to avoid the phenomenon of losing edges as far as possible. Then the search operator based on superstructure is given, the random selection rule of the initial network and the stochastic optimization strategy of the initial network are analyzed, and the learning algorithm of SSRandom structure based on the random search of superstructure is put forward emphatically. To some extent, the algorithm can jump out of the local optimal extremum. Finally, on the standard Survey,Asia,Sachs network, through the sensitivity, specificity, Euclidean distance and the overall accuracy of four evaluation indicators, and compared with the existing three hybrid learning algorithm of the experimental analysis, to verify the good performance of the learning algorithm.
【作者单位】： 山西大学计算智能与中文信息处理教育部重点实验室;山西财经大学信息管理学院;
【基金】：国家自然科学基金重点项目(61432011) 军民共用重大研究计划联合基金重点项目(U1435212) 国家自然科学基金优秀青年科学基金项目(61322211);国家自然科学基金项目(61672332) 中国博士后科学基金项目(2016M591409) 山西省自然科学基金项目(2013011016-4,2014011022-2)~~
【分类号】：TP18
本文编号：2470674
[Abstract]:In recent years, Bayesian networks (Bayesian network,BN) play an increasingly important role in uncertain knowledge representation and probabilistic reasoning. Among them, BN structure learning is an important problem in BN reasoning. However, in the current two-stage hybrid learning algorithms of BN structure, there are some problems: the first stage of undirected superstructure learning has the problem of easily losing the weak relations; The second stage of mountain climbing search algorithm is prone to fall into the local optimal problem. In order to solve these two problems, firstly, Opt01ss algorithm is used to study the superstructure, so as to avoid the phenomenon of losing edges as far as possible. Then the search operator based on superstructure is given, the random selection rule of the initial network and the stochastic optimization strategy of the initial network are analyzed, and the learning algorithm of SSRandom structure based on the random search of superstructure is put forward emphatically. To some extent, the algorithm can jump out of the local optimal extremum. Finally, on the standard Survey,Asia,Sachs network, through the sensitivity, specificity, Euclidean distance and the overall accuracy of four evaluation indicators, and compared with the existing three hybrid learning algorithm of the experimental analysis, to verify the good performance of the learning algorithm.
【作者单位】： 山西大学计算智能与中文信息处理教育部重点实验室;山西财经大学信息管理学院;
【基金】：国家自然科学基金重点项目(61432011) 军民共用重大研究计划联合基金重点项目(U1435212) 国家自然科学基金优秀青年科学基金项目(61322211);国家自然科学基金项目(61672332) 中国博士后科学基金项目(2016M591409) 山西省自然科学基金项目(2013011016-4,2014011022-2)~~
【分类号】：TP18
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