Similarity and metrics in case‐based reasoning

Similarity is a core concept in case‐based reasoning (CBR), because case base building, case retrieval, and even case adaptation all use similarity or similarity‐based reasoning. However, there is some confusion using similarity, similarity measures, and similarity metrics in CBR, in particular in domain‐dependent CBR systems. This article attempts to resolve this confusion by providing a unified framework for similarity, similarity relations, similarity measures, and similarity metrics, and their relationship. This article also extends some of the well‐known results in the theory of relations to similarity metrics. It appears that such extension may be of significance in case base building and case retrieval in CBR, as well as in various applied areas in which similarity plays an important role in system behavior. © 2002 Wiley Periodicals, Inc.     

Maintaining Retrieval Knowledge in a Case-Based Reasoning System

The knowledge stored in a case base is central to the problem solving of a case-based reasoning (CBR) system. Therefore, case-base maintenance is a key component of maintaining a CBR system. However, other knowledge sources, such as indexing and similarity knowledge for improved case retrieval, also play an important role in CBR problem solving. For many CBR applications, the refinement of this retrieval knowledge is a necessary component of CBR maintenance. This article focuses on optimization of the parameters and feature selections/weights for the indexing and nearest-neighbor algorithms used by CBR retrieval. Optimization is applied after case-base maintenance and refines the CBR retrieval to reflect changes that have occurred to cases in the case base. The optimization process is generic and automatic, using knowledge contained in the cases. In this article we demonstrate its effectiveness on a real tablet formulation application in two maintenance scenarios. One scenario, a growing case base, is provided by two snapshots of a formulation database. A change in the company's formulation policy results in a second, more fundamental requirement for CBR maintenance. We show that after case-base maintenance, the CBR system did indeed benefit from also refining the retrieval knowledge. We believe that existing CBR shells would benefit from including an option to automatically optimize the retrieval process.     

A unified logical model for CBR‐based e‐commerce systems

This article examines new issues resulting from applying case‐based reasoning (CBR) in e‐commerce and proposes a unified logical model for CBR‐based e‐commerce systems (CECS) that consists of three cycles and covers almost all activities of applying CBR in e‐commerce. This article also decomposes case adaptation into problem adaptation and solution adaptation, which not only improves the understanding of case adaptation in the traditional CBR, but also facilitates the refinement of activity of CBR in e‐commerce and intelligent support for e‐commerce. It then investigates CBR‐based product negotiation. This article thus gives insight into how to use CBR in e‐commerce and how to improve the understanding of CBR with its applications in e‐commerce from a logical viewpoint. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 29–46, 2005.     

基于偏好信息的案例检索算法

案例推理方法建立在“相似问题具有相似解”的基础上,能否从案例库中检索出与新问题“最相似”的案例是案例推理方法成功的关键因素之一。该文提出一种改进的检索方法,在原始最近相邻算法基础上,用专家对新问题案例与历史案例属性差异的效用评价替代原始的属性差异值来衡量专家对属性差异的敏感程度。引入变异系数来标度新问题案例与历史案例的属性差异的分布情况,从而保证检索出的最相似案例具有较高的属性差异的均衡性。通过具体案例检索实例分析,验证了该方法的有效性。     

An association-based case reduction technique for case-based reasoning

Case-based reasoning (CBR) is a type of problem solving technique which uses previous cases to solve new, unseen and different problems. Although a larger number of cases in the memory can improve the coverage of the problem space, the retrieval efficiency will be downgraded if the size of the case-base grows to an unacceptable level. In CBR systems, the tradeoff between the number of cases stored in the case-base and the retrieval efficiency is a critical issue. This paper addresses the problem of case-base maintenance by developing a new technique, the association-based case reduction technique (ACRT), to reduce the size of the case-base in order to enhance the efficiency while maintaining or even improving the accuracy of the CBR. The experiments on 12 UCI datasets and an actual case from Taiwan’s hospital have shown superior generalization accuracy for CBR with ACRT (CBR-ACRT) as well as a greater solving efficiency.     

Hierarchical case-based reasoning integrating case-based anddecompositional problem-solving techniques for plant-control softwaredesign

Case based reasoning (CBR) is an artificial intelligence technique that emphasises the role of past experience during future problem solving. New problems are solved by retrieving and adapting the solutions to similar problems, solutions that have been stored and indexed for future reuse as cases in a case-base. The power of CBR is severely curtailed if problem solving is limited to the retrieval and adaptation of a single case, so most CBR systems dealing with complex problem solving tasks have to use multiple cases. The paper describes and evaluates the technique of hierarchical case based reasoning, which allows complex problems to be solved by reusing multiple cases at various levels of abstraction. The technique is described in the context of Deja Vu, a CBR system aimed at automating plant-control software design     

Loss and gain functions for CBR retrieval

The method described in this article evaluates case similarity in the retrieval stage of case-based reasoning (CBR). It thus plays a key role in deciding which case to select, and therefore, in deciding which solution will be eventually applied. In CBR, there are many retrieval techniques. One feature shared by most is that case retrieval is based on attribute similarity and importance. However, there are other crucial factors that should be considered, such as the possible consequences of a given solution, in other words its potential loss and gain. As their name clearly implies, these concepts are defined as functions measuring loss and gain when a given retrieval case solution is applied. Moreover, these functions help the user to choose the best solution so that when a mistake is made the resulting loss is minimal. In this way, the highest benefit is always obtained.     

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.     

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．     

基于CBR和XML的软构件检索方法

在对现有构件检索方法分析的基础上，探讨了一种基于案例推理和XML技术的智能化软件构件的检索框架。重点阐述了构件案例库中构件的XML知识表示方法以及构件检索中需求构件和案例库中构件的相似度评估方法，提出了一种计算案例相似度的递归算法。     

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。     

基于案例推理的软测量方法及在磨矿过程中的应用

针对复杂工业过程中一些关键工艺参数难以用仪表进行在线检测的问题,提出了基于案例推理的软测量方法.案例表示由案例产生时间、工况描述、解及相似度组成;案例检索采用具有多相似度阈值计算的最近相邻策略;案例重用采用基于静态相似度阈值和基于动态相似度阈值两种算法,并给出了新的案例修正和存储策略.用该方法建立的磨矿粒度软测量模型已成功应用在某选矿厂磨矿过程中,应用结果表明提出的方法效果显著,具有推广应用前景.     

基于实例推理的智能刺绣编程系统

本文介绍基于实例推理的智能刺绣编程系统。根据电脑刺绣领域问题的需要，建立了描述刺绣样品的实例模型，利用动态存储模型技术实现实例的存储和检索，在此基础上给出了基于实例的推理流程和算法、实例重用和实例保留算法等。基于实例推理方法可大大提高绣品的质量和刺绣编程的效率。     

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。     

Introducing attribute risk for retrieval in case-based reasoning

One of the major assumptions in case-based reasoning is that similar experiences can guide future reasoning, problem solving and learning. This assumption shows the importance of the method used for choosing the most suitable case, especially when dealing with the class of problems in which risk, is relevant concept to the case retrieval process. This paper argues that traditional similarity assessment methods are not sufficient to obtain the best case; an additional step with new information must be performed necessary, after applying similarity measures in the retrieval stage. When a case is recovered from the case base, one must take into account not only the specific value of the attribute but also whether the case solution is suitable for solving the problem, depending on the risk produced in the final decision. We introduce this risk, as new information through a new concept called risk information that is entirely different from the weight of the attributes. Our article presents this concept locally and measures it for each attribute independently.     

P2P case storage and retrieval with an unspecified ontology

Traditional approaches for similarity-based retrieval of structured data, such as Case-Based Reasoning (CBR), have been largely implemented using centralized storage systems. In such systems, when the cases contain both numeric and free-text attributes, similarity-based retrieval cannot exploit standard speedup techniques based on multi-dimensional indexing, and the retrieval is implemented by an exhaustive comparison of the case to be solved with the whole set of stored cases. In this work, we review current research on Peer-to-Peer (P2P) and distributed CBR techniques and propose a novel approach for storage of the case-base in a decentralized Peer-to-Peer environment using the notion of Unspecified Ontology to improve the performance of the case retrieval stage and build CBR systems that can scale up to large case-bases. We develop an algorithm for efficient retrieval of approximated most-similar cases, which exploits inherent characteristics of the unspecified ontology in order to improve the performance of the case retrieval stage in the CBR problem solving cycle. The experiments show that the algorithm successfully retrieves cases close to the most-similar cases, while reducing the number of cases to be compared. Hence, it improves the performance of the retrieval stage. Moreover, the distributed nature of our approach eliminates the computational bottleneck and single point of failure of the centralized storage systems.     

From case-based reasoning to traces-based reasoning

CBR is an original AI paradigm based on the adaptation of solutions of past problems in order to solve new similar problems. Hence, a case is a problem with its solution and cases are stored in a case library. The reasoning process follows a cycle that facilitates “learning” from new solved cases. This approach can be also viewed as a lazy learning method when applied for task classification. CBR is applied for various tasks as design, planning, diagnosis, information retrieval, etc. The paper is the occasion to go a step further in reusing past unstructured experience, by considering traces of computer use as experience knowledge containers for situation based problem solving.     

Research of new strategies for improving CBR system

Case based reasoning (CBR), as an important AI technology, has gained popularity for its unique means of problem solving, which solves a new problem by remembering previous similar situations and reusing knowledge from the solutions to these situations. To construct a CBR system, two key issues have to be considered: one is feature selection, through which important features are extracted from the whole experience case and make up a case; the other is case retrieval, through which most appropriate case is retrieved for reuse. In order to further improve the accuracy of CBR system, this paper proposes a new feature selection method called Calculating Differences based on Growing Hierarchical Self Organizing Map clustering (CD-GHSOM) and a new case retrieval method called Growing Hierarchical Self Organizing Map based Case Retrieval (GHSOM-CR). Lots of experiments are implemented to validate the effectiveness of the proposed methods by comparing them with other recent researches.     

Ontology-based similarity for product information retrieval

Product development of today is becoming increasingly knowledge intensive. Specifically, design teams face considerable challenges in making effective use of increasing amounts of information. In order to support product information retrieval and reuse, one approach is to use case-based reasoning (CBR) in which problems are solved “by using or adapting solutions to old problems.” In CBR, a case includes both a representation of the problem and a solution to that problem. Case-based reasoning uses similarity measures to identify cases which are more relevant to the problem to be solved. However, most non-numeric similarity measures are based on syntactic grounds, which often fail to produce good matches when confronted with the meaning associated to the words they compare. To overcome this limitation, ontologies can be used to produce similarity measures that are based on semantics. This paper presents an ontology-based approach that can determine the similarity between two classes using feature-based similarity measures that replace features with attributes. The proposed approach is evaluated against other existing similarities. Finally, the effectiveness of the proposed approach is illustrated with a case study on product–service–system design problems.