【作者】 马壮；

【导师】 杨善林；

【作者基本信息】 合肥工业大学 ， 管理科学与工程， 2005， 硕士

【摘要】 在人工智能领域,不确定性问题一直成为人们关注和研究的焦点。贝叶斯网是自然、紧凑的联合概率分布的图形表示形式,反映了变量间的潜在的依赖关系,揭示了领域对象的内在结构。由于其具有很多优点,贝叶斯网已成为解决许多不确定性问题的强有力工具,成为人工智能领域的研究热点。 贝叶斯网的关键在于建立网络,而由专家给出的贝叶斯网带有主观性和不确定性,因此从数据中学习成为可行的和必要的建网方法。 本文主要研究完备数据集的贝叶斯网结构学习,在研究和分析现有结构学习算法的基础上,将ACO算法和K2评分标准引入到基于打分的结构学习算法中。主要研究内容如下: (1) 基于ACO的贝叶斯网结构学习:本文将ACO算法作为搜索算法,K2评分标准作为评分函数,针对结构学习中的节点排序和建网提出了AntOrdering算法和ACO B算法,并且针对ACO算法的变化形式,对ACO B算法加以讨论。 (2) 贝叶斯网在CRM中应用:将贝叶斯网应用到CRM的客户分析(数据挖掘)模块中,利用贝叶斯网对客户信息加以分析和萃取,从中获得所需的信息,以满足决策者的需求。更多还原

【Abstract】 In AI, uncertainty reasoning has been a focus of research. Bayesian Network is natural compact graphical representation of joint probability distribution, which can express a potential dependent relationship among uncertain variables and can exploit the structure of the domain. Because of its merits, Bayesian Network has been a powerful tool to solve many uncertainty problems, and become a mainstream within the AI probabilistic and uncertainty community.The first task of applying BN is construction. Nonetheless, it is often difficult, subjective and time-consuming to construct BN from expert knowledge alone, particularly because of the need to provide numerical parameters. Methods for capturing available data to construct BN or refine an expert-provides network promise to greatly improve both the efficiency of knowledge engineering and the accuracy of the methods.In this paper, we learn BN structure from complete data. On the basis of the research and analysis of the current BN structure learning algorithms, we introduce ACO algorithm and I<2 metric into score-based BN structure learning algorithm. The details are given as follows.(1) ACO-based BN structure learning algorithm: We use ACO algorithm as search procedure and K2 metric as score metric. The search space in which the ACO algorithms operate can be defined in two different ways: ordering and dag. We propose Ant ordering algorithm in ordering and ACO B algorithm in dag. We discuss the different variants of ACO B algorithm in this paper.(2) BN application in CRM: BN is applied to the part of customer analyzing (Data mining) in CRM. We use BN into eliciting useful information from customer database in order to satisfying the needs of the decision-maker.更多还原