小数据集条件下基于双重约束的BN参数学习

针对小数据集条件下的贝叶斯网络（Bayesian network,BN）参数学习问题,提出了一种基于双重约束的贝叶斯网络参数学习方法. 首先,对网络中的参数进行分析并将网络中的参数划分为: 父节点组合状态相同而子节点状态不同的参数和父节点组合状态不同而子节点状态相同的参数;然后,针对第一类参数提出了一种新的基于Beta分布拟合的贝叶斯估计方法,而针对第二类参数利用已有的保序回归估计方法进行学习,进而实现了对网络中参数的双重约束学习;最后,通过仿真实例说明了基于双重约束的参数学习方法对小数据集条件下贝叶斯网络参数学习精度提高的有效性.     

一种小规模数据集下的贝叶斯网络学习方法及其应用

提出了一种小规模数据集下学习贝叶斯网络的有效算法—FCLBNo FCLBN利用bootstrap方法在给定的小样本数据集上进行重抽样,然后用在抽样后数据集上学到的贝叶斯网络来佑计原数据集上的贝叶斯网络的高置信度的特征,并用这些特征来指导在原数据集上的贝叶斯网络搜索。用标准的数据集验证了FCLBN的有效性,并将FCLBN应用于酵母菌细胞中蛋白质的定位预测。实验结果表明,FCLBN能够在小规模数据集上学到较好的网络模型。     

小数据集条件下的多态系统贝叶斯网络参数学习

针对贝叶斯网络中多父节点条件概率分布参数学习问题,提出了一种适用于多态节点、模型不精确、样本信息不充分情形的参数学习方法.该方法利用因果机制独立假设,分解条件概率分布,使条件概率表的规模表现为父节点个数和状态数的线性形式;利用Leaky Noisy-MAX模型量化了多态系统模型未含因素对参数学习的影响;从小样本数据集中获取模型参数并合成条件概率表.结果表明,该方法能提高参数学习效率与精度.     

基于节点块序列约束的局部贝叶斯网络结构搜索算法

针对K2算法过度依赖节点序和节点序搜索算法评价节点序效率较低的问题, 提出一种基于节点块序列约束的局部贝叶斯网络结构搜索算法, 该算法首先通过评分定向构建定向支撑树结构, 在此基础上构建节点块序列, 然后利用节点块序列确定每个节点的潜在父节点集, 通过搜索每个节点的父节点集构建网络结构, 最后对该结构进行非法结构修正得到最优贝叶斯网络结构.利用标准网络将算法与几种不同类型的改进算法进行对比分析, 验证该算法的有效性.     

小数据集的贝叶斯网络结构学习

针对直接基于小数据集贝叶斯网络结构学习不可靠, 以及目前对小数据集的处理只强调扩展而忽略对扩展数据的修正等, 提出了将扩展与修正相结合的小数据集处理机制, 以及在此基础上的基于结点排序和局部打分--搜索的贝叶斯网络结构学习方法. 可不需要完全结点顺序的先验知识, 但能够结合专家的部分结点顺序信息. 实验结果显示了这种方法的有效性和可靠性.     

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.     

基于高斯核函数的朴素贝叶斯分类器依赖扩展

朴素贝叶斯分类器不能有效地利用属性之间的依赖信息, 而目前所进行的依赖扩展更强调效率, 使扩展后分类器的分类准确性还有待提高. 针对以上问题, 在使用具有平滑参数的高斯核函数估计属性密度的基础上, 结合分类器的分类准确性标准和属性父结点的贪婪选择, 进行朴素贝叶斯分类器的网络依赖扩展. 使用UCI 中的连续属性分类数据进行实验, 结果显示网络依赖扩展后的分类器具有良好的分类准确性.

     

传感器网络中数据驱动的睡眠调度机制

在能量受限的传感器网络中,尽量延长网络寿命同时保证服务质量(如感知覆盖和数据完整)是关键的研究问题.节点睡眠调度能有效延长网络寿命.研究数据驱动的睡眠调度机制,利用感知数据的时空相关性识别冗余节点.核心思想是用非参数回归方法为节点建立预测模型,求解最大支配数的节点支配集,调度多个支配集轮流工作.睡眠节点的数据可以由支配集节点恢复.分别给出集中式、半分布式和分布式3个睡眠调度方法.据知,这是第1个将统计回归模型用于睡眠调度并扩展到大规模网络的研究.实验结果表明,该方法能够有效地减少活跃节点个数,节省能耗从而延长网络寿命,同时在用户指定误差范围内保证数据的完整性.     

并行的贝叶斯网络参数学习算法

针对大样本条件下EM算法学习贝叶斯网络参数的计算问题,提出一种并行EM算法(Parallel EM,PL-EM)提高大样本条件下复杂贝叶斯网络参数学习的速度.PL-EM算法在E步并行计算隐变量的后验概率和期望充分统计因子;在M步,利用贝叶斯网络的条件独立性和完整数据集下的似然函数可分解性,并行计算各个局部似然函数.实验结果表明PL-EM为解决大样本条件下贝叶斯网络参数学习提供了一种有效的方法.     

混合多元回归学习模型的改进

研究统计方法分析问题,针对在实际应用外特性模型的输入普遍为混合变量,既包括连续随机变量,也包括离散随机变量.目前已有混合多元回归学习模型大多只处理连续随机变量,且有着多重共线性的缺陷.针对上述问题,研究了基于贝叶斯网络的回归树学习模型.基于贝叶斯网络的回归树学习模型的研究方法建立在朴素贝叶斯网络模型基础上,采用分而治之的原则构造决策树,以朴素贝叶斯取代叶节点.在2个UCI机器学习数据集上的仿真实验结果表明模型性能良好.基于贝叶斯网络的回归树学习模型可以有效减小预测误差.     

Causal reasoning in computer programs for medical diagnosis

Over the last decade substantial advances have been made in the use of causal pathophysiological knowledge in artificial intelligence-based programs for medical diagnosis. Various forms of causal representations have been used. They include probabilistic models, quantitative models, qualitative models, and models that describe causal relations at multiple levels of detail. This paper briefly analyses these methods using three representative systems. Outstanding problems and possible direction in further exploitation of causal reasoning for medical decision-support systems are also discussed.     

THE SCOPE OF DIMENSIONAL ANALYSIS IN QUALITATIVE REASONING

Dimensional analysis, traditionally used in physics and engineering to identify quantitative relationships, has recently been applied to qualitative reasoning of physical systems. We illustrate some problems of this approach. In the light of this, we reexamine the fundamentals of dimensional analysis in order to more precisely characterize its scope and limitations as a tool in qualitative reasoning. We also explore its relationship to state equation representations of physical systems. In particular, we describe its value in providing a set of constraints to reduce the ambiguity that bedevils qualitative reasoning schemes. We argue that dimensional analysis should not be seen as a substitute for knowledge about the physics but rather a supplement to other sources of knowledge.     

Mining the Semantic Web

In the Semantic Web vision of the World Wide Web, content will not only be accessible to humans but will also be available in machine interpretable form as ontological knowledge bases. Ontological knowledge bases enable formal querying and reasoning and, consequently, a main research focus has been the investigation of how deductive reasoning can be utilized in ontological representations to enable more advanced applications. However, purely logic methods have not yet proven to be very effective for several reasons: First, there still is the unsolved problem of scalability of reasoning to Web scale. Second, logical reasoning has problems with uncertain information, which is abundant on Semantic Web data due to its distributed and heterogeneous nature. Third, the construction of ontological knowledge bases suitable for advanced reasoning techniques is complex, which ultimately results in a lack of such expressive real-world data sets with large amounts of instance data. From another perspective, the more expressive structured representations open up new opportunities for data mining, knowledge extraction and machine learning techniques. If moving towards the idea that part of the knowledge already lies in the data, inductive methods appear promising, in particular since inductive methods can inherently handle noisy, inconsistent, uncertain and missing data. While there has been broad coverage of inducing concept structures from less structured sources (text, Web pages), like in ontology learning, given the problems mentioned above, we focus on new methods for dealing with Semantic Web knowledge bases, relying on statistical inference on their standard representations. We argue that machine learning research has to offer a wide variety of methods applicable to different expressivity levels of Semantic Web knowledge bases: ranging from weakly expressive but widely available knowledge bases in RDF to highly expressive first-order knowledge bases, this paper surveys statistical approaches to mining the Semantic Web. We specifically cover similarity and distance-based methods, kernel machines, multivariate prediction models, relational graphical models and first-order probabilistic learning approaches and discuss their applicability to Semantic Web representations. Finally we present selected experiments which were conducted on Semantic Web mining tasks for some of the algorithms presented before. This is intended to show the breadth and general potential of this exiting new research and application area for data mining.     

Enhanced qualitative physical reasoning system: Qupras

There are many expert systems that use experimental knowledge for diagnostic analysis and design. However, there are two problems for systems using only experiential knowledge:

1. unexpected problems cannot be solved and
2. acquiring experiential knowledge from human experts is difficult.

To solve these problems, general principles or basic knowledge must be added to expert systems in addition to the experimental knowledge. In response, we previously proposed Qupras (Qualitative physical reasoning system) as a framework for basic knowledge. This system has two knowledge representations, one related to physical laws and the other to objects. By using this knowledge, Qupras reasons about the relations among physical objects, and predicts the next state of a physical phenomenon. Recently, we have improved some of Qupras’ features, and this pater desctibes the following main enhancements:

1. inheritance for representation of objects,
2. new primitive representations to describe discontinuous change, and
3. control features for effective reasoning.

     

Continuous case-based reasoning

Case-based reasoning systems have traditionally been used to perform high-level reasoning in problem domains that can be adequately described using discrete, symbolic representations. However, many real-world problem domains, such as autonomous robotic navigation, are better characterized using continuous representations. Such problem domains also require continuous performance, such as on-line sensorimotor interaction with the environment, and continuous adaptation and learning during the performance task. This article introduces a new method for continuous case-based reasoning, and discusses its application to the dynamic selection, modification, and acquisition of robot behaviors in an autonomous navigation system, SINS (self-improving navigation system). The computer program and the underlying method are systematically evaluated through statistical analysis of results from several empirical studies. The article concludes with a general discussion of case-based reasoning issues addressed by this research.     

事件关系表示模型

事件关系的表示及事件推理是基于事件的知识处理的核心内容。文章提出了事件影响因子的概念来刻画事件间相互影响的强弱,给出了一种事件影响因子的计算方法。在此基础上,建立了事件关系图ERM(Event Relationship Map)来描述领域中事件之间的关系。依据事件关系和事件要素可以进行事件推理,重点阐述了ERM上基于关系的事件推理算法。最后,做了一个事件关系推理的实验,结果证明所提模型及算法与人的主观判断相一致,是合理可行的。     

基于模糊框架-产生式知识表示及推理研究

知识表示是专家系统求解能力及正确性的基础。针对不同知识表示方法的局限性,采用框架与产生式知识表示法结合表示专家知识。同时鉴于传统知识表示及推理方法在描述事实生产中不确定知识及经验中的缺陷问题,将模糊推理与知识表示相结合,应用模糊因子,定量细化描述模糊知识;并结合知识表示特点应用动态加权平均匹配函数及模糊推理方法,提出基于模糊框架-产生式知识表示方法及推理的研究,量化地表示知识及推理过程,为决策人员提供更加直观、准确的推理依据。     

Heterogeneous relational reasoning in knowledge graphs with reinforcement learning

Path-based relational reasoning over knowledge graphs has become increasingly popular due to a variety of downstream applications such as question answering in dialogue systems, fact prediction, and recommendation systems. In recent years, reinforcement learning (RL) based solutions for knowledge graphs have been demonstrated to be more interpretable and explainable than other deep learning models. However, the current solutions still struggle with performance issues due to incomplete state representations and large action spaces for the RL agent. We address these problems by developing HRRL (Heterogeneous Relational reasoning with Reinforcement Learning), a type-enhanced RL agent that utilizes the local heterogeneous neighborhood information for efficient path-based reasoning over knowledge graphs. HRRL improves the state representation using a graph neural network (GNN) for encoding the neighborhood information and utilizes entity type information for pruning the action space. Extensive experiments on real-world datasets show that HRRL outperforms state-of-the-art RL methods and discovers more novel paths during the training procedure, demonstrating the explorative power of our method.     

WFPN在模糊知识推理中的应用研究

WFPN用于知识推理方面和其他方法相比,其优点在于使得知识可以结构化表示,并且WFPN还具有系统的分析方法支持模糊推理。介绍了WFPN推理的基本概念,给出一个消除循环推理路径的算法,并在此基础上提出一个在可能存在循环推理路径下的推理算法。