基于贝叶斯网络的不确定性知识处理研究

贝叶斯网络因其在处理不确定性知识方面的优势近来受到数据挖掘等领域的重视。与当前流行的数据挖掘算法包括决策树、神经网络和遗传算法等相比,贝叶斯网络更易于理解,且有很好的预测效果,适用于处理那些本身存在着固有的不确定性的领域。在比较了贝叶斯网络处理不确定性知识的优势的基础上,描述了用贝叶斯网络进行数据挖掘的过程及其主要研究方向,最后对贝叶斯网络的应用领域、研究现状和前景进行了分析和展望。     

一个网页自动分类系统的设计

本文介绍了设计的一个网页自动分类系统。介绍了预处理 ,批量训练 ,特征选择 ,在线测试和重归档等模块的设计过程。系统采用有指导的学习方法 ,选取 Naive Bayes作分类模型和信息增益作为特征提取方法。测试结果表明 ,系统获得了较好的精度     

一种基于定性推理的网页分类方法

本文描述一种基于定性推理的网页分类方法,即通过对网页属性与样本集的相关性来得出结果。我们通过做实验对该方法进行了测试,获得了满意的结果。     

基于贝叶斯网络的本体不确定性推理

运用OWL语言扩展了本体对领域知识的不确定性表示,并基于贝叶斯网络实现了本体领域知识的不确定性推理。实验表明将贝叶斯网络与本体结合起来,能够充分发挥本体在知识描述方面的优势和贝叶斯网络的推理能力,实现依据部分信息的概率描述获取知识,指导实践。     

结合中文网页自动分类系统的AIP平台

AIP(All day Information Pursue)平台，即全天候信息跟踪平台，作为关注多方面消息的企业或团体查看Internet上新信息的一种解决方案，弥补了搜索引擎一些方面的不足。它能够从Internet上获取每日的新信息，利用网页自动分类去除不相关文章。通过此平台．用户可以按时间、按类别来查看信息，也可以对文章加以标注推荐给别人阅读。     

贝叶斯网络推理的研究与分析

贝叶斯网络是人工智能中不确定知识表示和推理的有力工具。介绍了贝叶斯网络的概念,给出一个实例,分析了贝叶斯网络推理的方法和过程。     

基于自动分类的网页机器人

随着互联网的普及和发展,网络上的信息资源越来越丰富,它需要高效智能的工具来完成信息资源的采集。WWW上的网页抓取器,又称Robot讨论了抓取器与文本自动分类器相结合,对用户要求领域网页的收集。抓取器找到相关链接进行抓取,而避免对非相关链接的抓取。这样可以节省硬件、网络资源和提高抓取器的效率。     

中文网页自动分类构架设计

本文主要分析了中文网页自动分类所要用到的功能和技术,给出了一个可行的中文网页自动分类系统的基本构架。     

结合中文网页自动分类系统的AIP平台

AIP(All day Information Pursue)平台,即全天候信息跟踪平台,作为关注多方面消息的企业或团体查看Internet上新信息的一种解决方案,弥补了搜索引擎一些方面的不足.它能够从Internet上获取每日的新信息,利用网页自动分类去除不相关文章.通过此平台,用户可以按时间、按类别来查看信息,也可以对文章加以标注推荐给别人阅读.     

一种基于Oracle Text的中文网页自动分类系统

Oracle Text是一种创建文本搜索和文档分类应用的技术。本文提出了一种基于该技术实现中文网页自动分类系统的解决方案。实验结果表明该方案准确有效,具有较好的性能,满足中文网页自动分类的需求。     

基于SVM的中文网页分类方法的研究

中文网页分类技术是数据挖掘中一个研究热点领域,而支持向量机(SVM)是一种高效的分类识别方法,在解决高维模式识别问题中表现出许多特有的优势.提出了基于支持向量机的中文网页分类方法,其中包括对该过程中的网页文本预处理、特征提取和多分类算法等关键技术的介绍.实验表明,该方法训练数据规模大大减少,训练效率较高,同时具有较好的精确率和召回率.     

Restricted Bayesian classification networks

Bayesian networks are graphical models that describe dependency relationships between variables, and are powerful tools for studying probability classifiers. At present, the causal Bayesian network learning method is used in constructing Bayesian network classifiers while the contribution of attribute to class is over-looked. In this paper, a Bayesian network specifically for classification-restricted Bayesian classification networks is proposed. Combining dependency analysis between variables, classification accuracy evaluation criteria and a search algorithm, a learning method for restricted Bayesian classification networks is presented. Experiments and analysis are done using data sets from UCI machine learning repository. The results show that the restricted Bayesian classification network is more accurate than other well-known classifiers.     

Representing and processing lineages over uncertain data based on the Bayesian network

Processing lineages (also called provenances) over uncertain data consists in tracing the origin of uncertainty based on the process of data production and evolution. In this paper, we focus on the representation and processing of lineages over uncertain data, where we adopt Bayesian network (BN), one of the popular and important probabilistic graphical models (PGMs), as the framework of uncertainty representation and inferences. Starting from the lineage expressed as Boolean formulae for SPJ (Selection–Projection–Join) queries over uncertain data, we propose a method to transform the lineage expression into directed acyclic graphs (DAGs) equivalently. Specifically, we discuss the corresponding probabilistic semantics and properties to guarantee that the graphical model can support effective probabilistic inferences in lineage processing theoretically. Then, we propose the function-based method to compute the conditional probability table (CPT) for each node in the DAG. The BN for representing lineage expressions over uncertain data, called lineage BN and abbreviated as LBN, can be constructed while generally suitable for both safe and unsafe query plans. Therefore, we give the variable-elimination-based algorithm for LBN's exact inferences to obtain the probabilities of query results, called LBN-based query processing. Then, we focus on obtaining the probabilities of inputs or intermediate tuples conditioned on query results, called LBN-based inference query processing, and give the Gibbs-sampling-based algorithm for LBN's approximate inferences. Experimental results show the efficiency and effectiveness of our methods.     

基于贝叶斯网络的威胁识别

对威胁进行准确识别是威胁评估的重要内容之一,它涉及到许多不确定性因素.贝叶斯网络是处理不确定性知识的有效工具.根据威胁识别与贝叶斯网络的特点,提出了基于贝叶斯网络的威胁识别方法.首先简单介绍了贝叶斯网络及其优点,然后根据一个具体的实例,建立了威胁识别的贝叶斯网络模型,并阐述了贝叶斯网络用于威胁识别的推理流程.通过对实例的计算结果表明,利用贝叶斯网络能够准确识别威胁,并能有效地处理不确定性信息.     

基于Jetty的网页防篡改技术的设计和实现

本文提出并实现了架设在符合J2EE标准的Web服务器上的网页防篡改系统,该系统主要采用Java语言开发和实现,能够对目前各种主流操作系统平台上的网页内容进行实时监控,发现网页被篡改时能及时恢复并报警,有效的保护目标网站的安全。     

Bayesian network classifiers for probability-based metrics

A large number of distance metrics have been proposed to measure the difference of two instances. Among these metrics, Short and Fukunaga metric (SFM) and minimum risk metric (MRM) are two probability-based metrics which are widely used to find reasonable distance between each pair of instances with nominal attributes only. For simplicity, existing works use naive Bayesian (NB) classifiers to estimate class membership probabilities in SFM and MRM. However, it has been proved that the ability of NB classifiers to class probability estimation is poor. In order to scale up the classification performance of NB classifiers, many augmented NB classifiers are proposed. In this paper, we study the class probability estimation performance of these augmented NB classifiers and then use them to estimate the class membership probabilities in SFM and MRM. The experimental results based on a large number of University of California, Irvine (UCI) data-sets show that using these augmented NB classifiers to estimate the class membership probabilities in SFM and MRM can significantly enhance their generalisation ability.     

Intelligent fault inference for rotating flexible rotors using Bayesian belief network

Flexible rotor is a crucial mechanical component of a diverse range of rotating machineries and its condition monitoring and fault diagnosis are of particular importance to the modern industry. In this paper, Bayesian belief network (BBN) is applied to the fault inference for rotating flexible rotors with attempt to enhance the reasoning capacity under conditions of uncertainty. A generalized three-layer configuration of BBN for the fault inference of rotating machinery is developed by fully incorporating human experts’ knowledge, machine faults and fault symptoms as well as machine running conditions. Compared with the Naive diagnosis network, the proposed topological structure of causalities takes account of more practical and complete diagnostic information in fault diagnosis. The network tallies well with the practical thinking of field experts in the whole processes of machine fault diagnosis. The applications of the proposed BBN network in the uncertainty inference of rotating flexible rotors show good agreements with our knowledge and practical experience of diagnosis.     

Bayesian network based business information retrieval model

The quality of business information can significantly affect the operation level of enterprise. This paper analyses the problem of business information retrieval (BIR). A Bayesian Network Based business information retrieval model (BN-BIRM) is proposed by means of Bayesian network (BN) and information retrieval (IR) theory and a method for query adaptation is presented. In this model the customized query requirement of enterprise (CQR) is expressed in terms of the predefined illustrative documents related to business domain. The similarities between the documents and the query are evaluated with the conditional probabilities among the nodes in the BN. In the experiments, BN-BIRM is compared with the Belief Network model based on vector space model (VSM) ranking strategy and the Inference Network model based on TF-IDF ranking strategy. The experimental results show that BN-BIRM is effective for collecting business information on a large scale.