贝叶斯网络拓扑结构确定方法的研究

贝叶斯方法是概率统计学中一种很重要的方法 ,贝叶斯网络是一种将贝叶斯概率方法和有向无环图的网络拓扑结构有机结合的表示模型 ,描述了数据项及其依赖关系 ,并根据各个变量之间概率关系建立的图论模型 ,但是如何获取它的网络拓扑结构是一个需要解决的问题 ,本文提出一种如何寻找最有可能的贝叶斯网络模型方法 ,并用启发式算法进行模型评估     

Improving Bayesian Network Structure Learning with Mutual Information-Based Node Ordering in the K2 Algorithm

Structure learning of Bayesian networks is a well-researched but computationally hard task. We present an algorithm that integrates an information-theory-based approach and a scoring-function-based approach for learning structures of Bayesian networks. Our algorithm also makes use of basic Bayesian network concepts like d-separation and condition independence. We show that the proposed algorithm is capable of handling networks with a large number of variables. We present the applicability of the proposed algorithm on four standard network data sets and also compare its performance and computational efficiency with other standard structure-learning methods. The experimental results show that our method can efficiently and accurately identify complex network structures from data.     

Bayesian networks based rare event prediction with sensor data

A Bayesian network is a powerful graphical model. It is advantageous for real-world data analysis and finding relations among variables. Knowledge presentation and rule generation, based on a Bayesian approach, have been studied and reported in many research papers across various fields. Since a Bayesian network has both causal and probabilistic semantics, it is regarded as an ideal representation to combine background knowledge and real data. Rare event predictions have been performed using several methods, but remain a challenge. We design and implement a Bayesian network model to forecast daily ozone states. We evaluate the proposed Bayesian network model, comparing it to traditional decision tree models, to examine its utility.     

Reasoning composite beliefs using a qualitative approach

A Bayesian network is a knowledge representation technique for use in expert system development. The probabilistic knowledge encoded in a Bayesian network is a set of composite hypotheses expressed over the permutation of a set of variables (propositions). Ordering these composite hypotheses according to their a posteriori probabilities can be exponentially hard. This paper presents a qualitative reasoning approach which takes advantage of certain types of topological structures and probability distributions of a Bayesian network to derive the partial ordering of composite hypotheses. Such an approach offers an attractive alternative to reduce the computational complexity of deriving a partial ordering in which consistency is guaranteed.This work is supported in part by a grant to Queens College from the General Research Branch, National Institute of Health under grant No. RR-07064.     

Collective Mining of Bayesian Networks from Distributed Heterogeneous Data

We present a collective approach to learning a Bayesian network from distributed heterogeneous data. In this approach, we first learn a local Bayesian network at each site using the local data. Then each site identifies the observations that are most likely to be evidence of coupling between local and non-local variables and transmits a subset of these observations to a central site. Another Bayesian network is learnt at the central site using the data transmitted from the local site. The local and central Bayesian networks are combined to obtain a collective Bayesian network, which models the entire data. Experimental results and theoretical justification that demonstrate the feasibility of our approach are presented.16 November 2001     

基于共轭先验分布的贝叶斯网络分类模型

针对贝叶斯网络后验概率需计算样本边际分布,计算代价大的问题,将共轭先验分布思想引入贝叶斯分类,提出了基于共轭先验分布的贝叶斯网络分类模型.针对非区间离散样本,提出一种自适应的样本离散方法,将小波包提取模拟电路故障特征离散化作为分类模型属性.仿真验证表明,模型分类效果较好,算法运行速度得以提高,也可应用于连续样本和多分类的情况,扩展了贝叶斯网络分类的应用范围.     

Bayes网络学习的MCMC方法

基于Bayes统计理论, 提出了一种从数据样本中学习Bayes网络的Markov链Monte Carlo(MCMC)方法. 首先通过先验概率和数据样本的结合得到未归一化的后验概率, 然后使用此后验概率指导随机搜索算法寻找“好”的网络结构模型. 通过对Alarm网络的学习表明了本算法具有较好的性能.     

Bayes文本分类器的改进方法研究

在文本分类领域，Bayes分类器是一种常用且效果较好的、基于概率的分类器，具有较严密的理论基础。该文对朴素Bayes文本分类器进行了分析，提出了利用权值调整机制改善其分类性能的方法，以及在缺乏大量训练文本的情况下，利用EM算法进行非监督Bayes分类的方法，并讨论了如何运用启发式方法确定Bayes网络结构，在更贴近真实环境的情况下进行文本分类。     

用于态势评估中因果推理的贝叶斯网络

1 引言贝叶斯网络是由R.Howard和J.Matheson于1981年提出来的,它主要用来表述不确定的专家知识。后来经过J.Pearl,D.Heckerman等人的研究,贝叶斯网络的理论及算法有了很大的发展。作为一种知识表示和进行概率推理的框架,贝叶斯网络在具有内在不确定性的推理和决策问题中已经得到了广泛的应用,例如概率专家系统、计算机视觉和数据挖掘等。     

贝叶斯网学习中一种有效的爬山算法

提出在学习贝叶斯网下的一种行之有效的爬山算法,HCBest算法.该算法在学习网络结构形成环时,选择删除能提高打分值最多的边,直到没有环为止.实验证明,HCBest既可以作为一种独立的贝叶斯网学习方法,又可以作为其它复杂元启发方法的局部搜索算法.HCBest学出的网络在打分质量和结构上都比较好.在算法的简洁性和稳定性方面,HCBest的表现也令人满意.     

An efficient data mining method for learning Bayesian networks using an evolutionary algorithm-based hybrid approach

Given the explosive growth of data collected from current business environment, data mining can potentially discover new knowledge to improve managerial decision making. This paper proposes a novel data mining approach that employs an evolutionary algorithm to discover knowledge represented in Bayesian networks. The approach is applied successfully to handle the business problem of finding response models from direct marketing data. Learning Bayesian networks from data is a difficult problem. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second one searches good network structures according to a metric. Unfortunately, both approaches have their own drawbacks. Thus, we propose a novel hybrid algorithm of the two approaches, which consists of two phases, namely, the conditional independence (CI) test and the search phases. In the CI test phase, dependency analysis is conducted to reduce the size of the search space. In the search phase, good Bayesian network models are generated by using an evolutionary algorithm. A new operator is introduced to further enhance the search effectiveness and efficiency. In a number of experiments and comparisons, the hybrid algorithm outperforms MDLEP, our previous algorithm which uses evolutionary programming (EP) for network learning, and other network learning algorithms. We then apply the approach to two data sets of direct marketing and compare the performance of the evolved Bayesian networks obtained by the new algorithm with those by MDLEP, the logistic regression models, the na/spl inodot//spl uml/ve Bayesian classifiers, and the tree-augmented na/spl inodot//spl uml/ve Bayesian network classifiers (TAN). In the comparison, the new algorithm outperforms the others.     

Remote sensing data change detection based on the CI test of Bayesian networks

In recent years, the Bayesian network approach has been used as a data-mining tool in many information fields, but it has rarely been used to process remote sensing data. In this paper, we introduce a Bayesian network classifier for remote sensing data change detection. Using the conditional independence (CI) test we can find out relationships among the attributes and construct a Bayesian network that incorporates these relationship constraints.After geometric correction and radiometric normalization, a Bayesian network change detection system based on CI test algorithm is developed and applied to two temporal Landsat TM data acquired in 1994 and 2003 of Beijing area, and the overall change detection classification accuracy can get 92%. The experimental results show that Bayesian network is a newly effective approach for remote sensing data change detection.     

Sensitivity analysis on causal chains of Bayesian networks

To analyze the key path of Bayesian network in complex systems, this study proposes to analyze the sensitivity of causal chains of Bayesian networks using the Petri net structural analysis approach to obtain the key chain through which the cause influences the consequence. First, the Bayesian network is transformed into Petri net, the structural analysis approach of which is employed to analyze structural nature of the Bayesian network, ensuring correctness of the constructed Bayesian network structure. Then based on the above fact that the structure is correct, S‐invariants of a Petri net is used to search for simple causal chains of the Bayesian network. Finally, the causal effect is defined and sensitivity analysis is made on the causal chains. The said method is applied to MDS causal chain analysis. Results show that the proposed method is direct viewing and practical. This method has some reference value for decision making in complex systems. © 2011 Wiley Periodicals, Inc.     

利用贝叶斯网络进行遥感变化检测

多时相遥感信息变化检测及其算法探索是当前国际遥感领域研究的热点,但是贝叶斯网络在遥感数据分类、特别是应用在变化检测方面的文献却很少。本文介绍了利用贝叶斯网络的变量间独立性测试原理,构建了输入两个时相多波段遥感信息的有向无环结构,利用训练后的网络进行两个时相多波段遥感变化信息的检测,取得了较好的效果。对北京六环线以内区域,1994年、2003年5种地类变化信息的遥感数据检测和类型转换进行了统计,其中耕地转换为城镇的占整个区域的26.52%,绿地增加占整个区域4.68%,水体减少占整个区域6.78%,导致裸地增加占整个区域4.80%,这个结果也在1∶5万的航空影像和地面上得到了验证。实验结果表明,贝叶斯网络为遥感数据的直接变化检测提供了一种新的途径。     

Mining Bayesian networks out of ontologies

Probabilistic reasoning is an essential feature when dealing with many application domains. Starting with the idea that ontologies are the right way to formalize domain knowledge and that Bayesian networks are the right tool for probabilistic reasoning, we propose an approach for extracting a Bayesian network from a populated ontology and for reasoning over it. The paper presents the theory behind the approach, its design and examples of its use.     

基于先验节点序学习贝叶斯网络结构的优化方法

针对小样本数据集下学习贝叶斯网络 (Bayesian networks, BN)结构的不足, 以及随着条件集的增大, 利用统计方法进行条件独立 (Conditional independence, CI) 测试不稳定等问题, 提出了一种基于先验节点序学习网络结构的优化方法. 新方法通过定义优化目标函数和可行域空间, 首次将贝叶斯网络结构学习问题转化为求解目标函数极值的数学规划问题, 并给出最优解的存在性及唯一性证明, 为贝叶斯网络的不断扩展研究提出了新的方案. 理论证明以及实验结果显示了新方法的正确性和有效性.     

一种基于Bayesian信念网络的客户行为预测方法

提出一种基于Bayesian信念网络（BN）的客户行为预测方法．通过知识学习构建客户行为Bayesian网络（CBN）,根据CBN对预实例计算联合分布概率,准确预测了一对一营销优化中的客户行为．CBN学习算法包括连线和定向部分,复杂度为O（N^4）条件相关测试．在零售行业一对一营销实际应用表明,CBN学习算法较现有BN学习算法更快构建CBN,预测精度高于朴素Bayesina分类法．     

Assessing new product development project risk by Bayesian network with a systematic probability generation methodology

New product development (NPD) is a crucial process to keep a company being competitive. However, because of its inherent features, NPD is a process with high risk as well as high uncertainty. To ensure a smooth operation of NPD, the risk involved in the process need to be assessed and the uncertainty should also be addressed properly. Facing these two tasks, in this paper, the critical risk factors in NPD are first analyzed. Since Bayesian network is specialized in dealing with uncertainties, those risk factors are then modeled into a Bayesian network to facilitate the assessing of the risk involved in an NPD process. To generate the probabilities of different kinds of nodes in a Bayesian network, a systematic probability generation approach is proposed with emphasis on generating the conditional probabilities of the nodes with multi-parents. A case study is also given in the paper to test and validate the critical risk factors as well as the probability generation approach.     

A method for explaining Bayesian networks for legal evidence with scenarios

In a criminal trial, a judge or jury needs to reason about what happened based on the available evidence, often including statistical evidence. While a probabilistic approach is suitable for analysing the statistical evidence, a judge or jury may be more inclined to use a narrative or argumentative approach when considering the case as a whole. In this paper we propose a combination of two approaches, combining Bayesian networks with scenarios. Whereas a Bayesian network is a popular tool for analysing parts of a case, constructing and understanding a network for an entire case is not straightforward. We propose an explanation method for understanding a Bayesian network in terms of scenarios. This method builds on a previously proposed construction method, which we slightly adapt with the use of scenario schemes for the purpose of explaining. The resulting structure is explained in terms of scenarios, scenario quality and evidential support. A probabilistic interpretation of scenario quality is provided using the concept of scenario schemes. Finally, the method is evaluated by means of a case study.     

Structure learning of Bayesian networks by genetic algorithms: aperformance analysis of control parameters

We present a new approach to structure learning in the field of Bayesian networks. We tackle the problem of the search for the best Bayesian network structure, given a database of cases, using the genetic algorithm philosophy for searching among alternative structures. We start by assuming an ordering between the nodes of the network structures. This assumption is necessary to guarantee that the networks that are created by the genetic algorithms are legal Bayesian network structures. Next, we release the ordering assumption by using a “repair operator” which converts illegal structures into legal ones. We present empirical results and analyze them statistically. The best results are obtained with an elitist genetic algorithm that contains a local optimizer