CBR快速检索算法在时间序列预测中的应用

随着CBR应用的推广,涉及越来越多的时态信息需要处理.探讨了一种基于时间序列数据的时态CBR,提出了一种基于卷积的时态CBR快速检索算法.其思路是利用时序范例之间的时间约束关系,去除检索中求取相似度的冗余计算,并利用卷积的傅立叶变换性质,在频域求解相似度以减少计算时间复杂度.实验证明.在匹配较长的序列时,快速算法可以显著的提高时态CBR的检索效率.在CBR快速检索算法的基础上,以证券价格预测问题作为应用,借鉴流形学习理论中LLE算法的思想,设计了一种基于时态CBR的时间序列预测算法.实验证明,这种基于时态CBR的时间序列预测方法与前述CBR快速检索算法相配合,取得了较好的预测效果和预测效率.     

一种支持时间序列数据的CBR检索算法

探讨了如何为CBR（基于范例的推理）增加对一种特殊的范例类型——时间序列数据的支持．分析了基于谱分析的时间序列相似度比较算法不适用于CBR检索的缺点，并在此基础上设计了一种综合性能很好的CBR检索算法．思路是把时间序列相似度比较转化成一个卷积问题，并用DFT来简化这个卷积的计算．通过对这种CBR检索算法进行了深入的理论分析和认真的实验，结果证明，提出的算法是一个高效的算法．在这个检索算法的基础上，CBR就能够席用到时序数据的分析推理中，具有广阔的应用前景．     

一种基于k-NN的案例相似度权重调整算法

对于CBR中的案例检索问题,结合经典案例相似度计算方法,对目前在各实际系统中应用最为广泛的k-NN算法进行改进。经过特征约简,在假设时间因素对历史案例可采纳程度有显著影响基础上,提出了一种小规模的基于时序的案例特征权重多阶段调整算法。该算法适用于数值型特征项相似度计算。     

基于范例推理的智能答疑的研究与实现

针对答疑系统在一定程度上依赖于专家知识和以往经验的特点,将CBR引入到答疑系统的设计中,研究了基于CBR的智能答疑系统范例库的构建方法,对BP神经网络和范例匹配算法在CBR范例库检索中的应用进行了分析.能有效地提高答疑系统的效率和准确性,进一步提高答疑系统的智能性.     

基于知识库和实例推理的构件检索方法

提出了一种利用人工智能领域中基于实例的推理（CBR）创建基于知识库的软件构件库进行构件检索的框架方法。重点阐述了利用软件构件的功能和行为知识表达测量检索到的构件实例与问题需求的相似度、构件功能性和构件可重用性的方法。     

一种k-means聚类的案例检索算法

针对CBR系统中案例检索算法存在的问题,根据k-means算法思想,将案例库进行聚类,在聚类基础上设计了一个案例检索算法。分析了样本案例的选取规则,重点论述了案例检索算法。根据实验结果表明,该方法能够有效地提高案例检索结果的召回率及案例检索效率。     

一种实时的智能机器人推理机制

为提高实例推理(CBR)机制的实时性,提出一种智能机器人推理机制。给出CBR推理流程,采用时间限定算法和向量空间法对其进行改进,以加快检索和完成学习,使用模块化部件搭建智能平台。实验结果表明,与CBR相比,该机制在完成相同任务时所使用的时间更少,得到的结果更加合理。     

基于本体的案例推理模型研究*

提出了基于本体的案例检索及相似性评估方法和基于本体的案例适配模型,使得CBR(case-based reasoning)系统的开发可在语义层次上进行相似性评估和案例适配,这样得到的结果更能反映用户的真实需求;并且CBR所需要的领域知识可从本体中获取,大大降低了传统CBR系统中知识获取的瓶颈。最后在此基础上,提出了基于本体的CBR系统模型框架,从软件复用的角度提高了CBR系统的开发效率。     

分布式知识系统中基于粗糙集的CBR检索方法

基于实例推理(Case-Based Reasoning,CBR)自20世纪80年代末、90年代初兴起之后,受到人工智能研究者的高度重视.CBR是一种相似推理方法,其核心在于用过去的实例和经验来解决问题[1].如何在CBR系统中高效地完成最相似实例的检索是CBR的关键问题之一,这对于新问题的求解效率和推理的准确性都有较大影响.     

基于ALCQ(D)的CBR事例修正算法研究

CBR(基于事例推理)是人工智能领域的一个分支,它克服了知识获取的瓶颈问题,事例修正是CBR的关键步骤。以ALC为代表的描述逻辑已被充分应用到CBR中,但目前在基于描述逻辑的CBR中还没有比较有效的算法来判断检索到的相似事例是否需要修正和如何进行修正。ALCQ(D)是在ALC的基础上引入定性数量约束Q和有型域D得到的。提出的算法用ALCQ(D)概念来描述CBR源事例和目标事例,先假定检索到的相似事例能够解决目标问题,即假定目标事例和相似事例同时满足知识库,但这样可能会与知识库产生冲突;接着使用冲突检测机制来查找相似事例概念描述中导致冲突的概念;最后使用概念替换规则在TBox本体库中检索该概念的最相似概念去替换它自己。研究表明,该算法具有界限性、可靠性和完备性。通过一个实例对其进行检验,结果表明,该算法可以准确修正检索到的相似事例,解决目标问题。     

Gaussian case-based reasoning for business failure prediction with empirical data in China

Case-based reasoning (CBR) is an easily understandable concept. Business failure prediction (BFP) is a valuable tool that can assist businesses take appropriate action when faced with the knowledge of the possibility of business failure. This study aims to improve the performance of a CBR system for BFP in terms of accuracy and reliability by constructing a new similarity measure, an area seldom researched in the domain of BFP. In order to fulfill this objective, we present a hybrid Gaussian CBR (GCBR) system and use it in BFP with empirical data in China. The heart of GCBR is similarity measure using Gaussian indicators. Feature distances between a pair of cases on each feature are transferred to Gaussian indicators by Gaussian transformations. A combiner is used to generate case similarity on the basis of the Gaussian indicators. Consensus of nearest neighbors is used to generate forecasting on the basis of case similarity. The new hybrid CBR system was empirically tested with data collected from the Shanghai Stock Exchange and Shenzhen Stock Exchange in China. We statistically validated our results by comparing them with multiple discriminant analysis, logistic regression, and two classical CBR systems. The results indicated that GCBR produces superior performance in short-term BFP of Chinese listed companies in terms of both predictive accuracy and coefficient of variation.     

基于CBR与灰色关联度的财务危机预警

提出一种基于案例推理(CBR)与灰色关联度的企业财务危机预警模型。将灰色关联分析应用于企业财务危机预警的案例推理中,采用特征属性的主客观权重计算案例相似度。根据各特征属性对案例检索的重要程度,通过权重向量排除非关键指标对案例判断的干扰。实验结果表明,该方法得到的案例相似性排序结果符合实际情况,可提高相似企业的检索效率,满足企业财务危机预警的要求。     

基于案例推理的纺纱工艺智能设计

针对工艺设计系统在一定程度上依赖于专家知识和以往经验的特点，将基于案例推理引入到纺纱工艺设计中，提出了一套基于CBR的智能化工艺设计系统，介绍了工艺案例之间的相似性度量算法。并讨论了案例库（casebase）的维护。实际应用结果表明，该智能设计系统有效地提高了工艺设计的效率和准确性，缩短了产品开发周期，具有重要的应用价值。     

基于实例推理的企业动态联盟伙伴选择与优化模型

将基于案例的推理方法运用于动态联盟伙伴企业选择与优化系统中，建立了伙伴企业选择系统的模型。具体讨论了方案库和评价结果库的建立，提出了基于灰色关联理论和模糊集理论相结合的相似度计算方法，从而可以准确地检索到相近案例，提高了伙伴企业选择的效率和准确性。     

基于卷积的时态CBR快速检索算法及应用*

探讨了如何增强CBR对一种常见的时态信息,即时间序列数据的检索能力;分析了已有的基于傅里叶频谱分析的时间序列检索算法应用于CBR时遇到的问题,并根据时态CBR检索的需要,提出了一种新的基于循环卷积和傅里叶变换时间序列检索算法.理论分析和数值实验结果都证明,提出的算法在检索效率上有一定的优势.将采取这种检索方法的时态CBR应用于时间序列的预测问题中,取得了较好的预测效果且具有较高的预测效率.     

基于灰色理论的故障案例检索算法

案例检索是基于案例推理系统的关键问题。为了在信息不完全的条件下提高故障案例匹配的准确度,将灰色关联分析应用到故障案例相似度的计算中,对灰色关联度公式进行改进,提出一种基于二次搜索策略的故障案例检索算法。对多种案例检索算法进行比较分析,结果表明该方法克服了传统方法确定相似度系数的主观性缺点,具有较好的分辨能力。     

An empirical study of predicting software faults with case-based reasoning

The resources allocated for software quality assurance and improvement have not increased with the ever-increasing need for better software quality. A targeted software quality inspection can detect faulty modules and reduce the number of faults occurring during operations. We present a software fault prediction modeling approach with case-based reasoning (CBR), a part of the computational intelligence field focusing on automated reasoning processes. A CBR system functions as a software fault prediction model by quantifying, for a module under development, the expected number of faults based on similar modules that were previously developed. Such a system is composed of a similarity function, the number of nearest neighbor cases used for fault prediction, and a solution algorithm. The selection of a particular similarity function and solution algorithm may affect the performance accuracy of a CBR-based software fault prediction system. This paper presents an empirical study investigating the effects of using three different similarity functions and two different solution algorithms on the prediction accuracy of our CBR system. The influence of varying the number of nearest neighbor cases on the performance accuracy is also explored. Moreover, the benefits of using metric-selection procedures for our CBR system is also evaluated. Case studies of a large legacy telecommunications system are used for our analysis. It is observed that the CBR system using the Mahalanobis distance similarity function and the inverse distance weighted solution algorithm yielded the best fault prediction. In addition, the CBR models have better performance than models based on multiple linear regression. Taghi M. Khoshgoftaar is a professor of the Department of Computer Science and Engineering, Florida Atlantic University and the Director of the Empirical Software Engineering Laboratory. His research interests are in software engineering, software metrics, software reliability and quality engineering, computational intelligence, computer performance evaluation, data mining, and statistical modeling. He has published more than 200 refereed papers in these areas. He has been a principal investigator and project leader in a number of projects with industry, government, and other research-sponsoring agencies. He is a member of the Association for Computing Machinery, the IEEE Computer Society, and IEEE Reliability Society. He served as the general chair of the 1999 International Symposium on Software Reliability Engineering (ISSRE’99), and the general chair of the 2001 International Conference on Engineering of Computer Based Systems. Also, he has served on technical program committees of various international conferences, symposia, and workshops. He has served as North American editor of the Software Quality Journal, and is on the editorial boards of the journals Empirical Software Engineering, Software Quality, and Fuzzy Systems. Naeem Seliya received the M.S. degree in Computer Science from Florida Atlantic University, Boca Raton, FL, USA, in 2001. He is currently a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. His research interests include software engineering, computational intelligence, data mining, software measurement, software reliability and quality engineering, software architecture, computer data security, and network intrusion detection. He is a student member of the IEEE Computer Society and the Association for Computing Machinery.     

基于案例推理系统中案例检索算法的探索

案例检索是基于案例推理系统的中心环节,目前应用最多的欧氏距离检索算法在实际应用中经常出现计算的相似度结果偏离工程实际的情况。文章利用一种归一化效用函数在最近邻法原理基础上提出一种改进的欧氏距离检索算法。实际工程应用结果说明,这种改进的算法不仅有效,而且简单实用。     

金融信用风险评估系统的实现和探讨

介绍了一种基于案例推理和规则推理机制的金融机构信用风险评估系统，结合了2种推理方法的优点，实现了企业信用风险的评估，讲解了该系统中CBR的研究、案例匹配相似度算法，提出了改进的最短距离算法和最短距离算法，介绍了CBR的维护方法——施加控制案例的添加和折半案例的删除方法，指出了该系统中存在的问题和进一步的研究方向。