A connectionist approach for similarity assessment in case-based reasoning systems

Case-Based Reasoning (CBR) systems support ill-structured decision making. In ill-structured decision environments, decision makers (DMs) differ in their problem solving approaches. As a result, CBR systems would be more useful if they were able to adapt to the idiosyncrasies of individual decision makers. Existing implementations of CBR systems have been mainly symbolic, and symbolic CBR systems are unable to adapt to the preferences of decision makers (i.e., they are static). Retrieval of appropriate previous cases is critical to the success of a CBR system. Widely used symbolic retrieval functions, such as nearest-neighbor matching, assume independence of attributes and require specification of their importance for matching. To ameliorate these deficiencies connectionist systems have been proposed. However, these systems are limited in their ability to adapt and grow. To overcome this limitation, we propose a distributed connectionist-symbolic architecture that adapts to the preferences of a decision maker and that, additionally, ameliorates the limitations of symbolic matching. The proposed architecture uses a supervised learning technique to acquire the matching knowledge. The architecture allows the growth of a case base without the involvement of a knowledge engineer. Empirical investigation of the proposed architecture in an ill-structured diagnostic decision environment demonstrated a superior retrieval performance when compared to the nearest-neighbor matching function.     

CBR方法在谈判中的研究与应用

NSS的辅助决策技术很多,CBR方法是很有效的一种。考虑增强案例推理系统的性能,论文在案例表示和组织方面展开研究。用ID3算法构建决策树,将谈判产生的历史案例通过分类形成知识库。在此基础上进行案例匹配,效率将有所提高。最后,通过一个应用实例,说明了该方法的可行性。     

基于范例的电子产品设计系统

基于范例的推理是人工智能领域应用较广的一种技术。本文成功地将基于范例的推理技术用于电子产品设计系统，并设计了功能模块级和器件级双层推理机制，引入余弦匹配函数。并用实例表明这是一个成功的应用。     

Global optimization of case-based reasoning for breast cytology diagnosis

Case-based reasoning (CBR) is one of the most popular prediction techniques in medical domains because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation – its prediction performance is generally lower than other AI techniques like artificial neural networks (ANN). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GA). Our model improves the prediction performance in three ways – (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating useless or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.     

A CASE-BASED DECISION SUPPORT SYSTEM FOR INDIVIDUAL STRESS DIAGNOSIS USING FUZZY SIMILARITY MATCHING

Stress diagnosis based on finger temperature (FT) signals is receiving increasing interest in the psycho-physiological domain. However, in practice, it is difficult and tedious for a clinician and particularly less experienced clinicians to understand, interpret, and analyze complex, lengthy sequential measurements to make a diagnosis and treatment plan. The paper presents a case-based decision support system to assist clinicians in performing such tasks. Case-based reasoning (CBR) is applied as the main methodology to facilitate experience reuse and decision explanation by retrieving previous similar temperature profiles. Further fuzzy techniques are also employed and incorporated into the CBR system to handle vagueness, uncertainty inherently existing in clinicians reasoning as well as imprecision of feature values. Thirty-nine time series from 24 patients have been used to evaluate the approach (matching algorithms) and an expert has ranked and estimated similarity. On average goodness-of-fit for the fuzzy matching algorithm is 90% in ranking and 81% in similarity estimation that shows a level of performance close to an experienced expert. Therefore, we have suggested that a fuzzy matching algorithm in combination with CBR is a valuable approach in domains, where the fuzzy matching model similarity and case preference is consistent with the views of domain expert. This combination is also valuable, where domain experts are aware that the crisp values they use have a possibility distribution that can be estimated by the expert and is used when experienced experts reason about similarity. This is the case in the psycho-physiological domain and experienced experts can estimate this distribution of feature values and use them in their reasoning and explanation process.     

案例推理变权值引擎模型及权值计算方法

在案例推理（CBR）案例检索匹配中,不同案例通常由不同的特征构成。而传统的CBR引擎模型大多采用固定权值模式,导致系统在匹配精度方面的性能很低。为了解决这一问题,提出一种CBR变权值引擎模型,在其特征权值计算模块引入人机互动机制,基于群决策法计算主观权值,提出依据专家个体和群体决策差异的主观权值调整方法;基于相似粗糙集法计算客观权值。最后设计了一种综合权值调整算法,通过计算主观权值和客观权值间的距离,判断两者的偏离程度,从而推导出权值调整系数,得到最终的权值调整结果。通过网络攻击案例进行的算例分析和仿真实验验证了上述方法的正确性和优越性。     

一种CBR与RBR相结合的快速预案生成系统

将范例推理(case based reasoning,CBR)与规则推理(rule based reasoning,RBR)两种人工智能技术相结合,实现一种快速预案生成系统.它有效地解决了单纯RBR系统在预案生成过程中的时间延迟缺陷和知识库难以获取的瓶颈.通过CBR工具,能够把以前发生的紧急事件和解决方案生成预案.一旦新的事件发生,首先从预案库中进行案例的相似性检索,如果没有检索到预案或者检索到的预案匹配度很低,再采用RBR系统对紧急事件进行规则推理,然后把推理结果重新存入预案库.实验数据表明,这种方法对单纯RBR系统在时间响应上进行了有效的优化.另外,因为案例的获取比专家系统推理规则的获取容易得多,它同时解决了RBR系统推理规则难以获取的瓶颈.根据这种思想,实现了CBR与RBR结合的快速预案生成系统.目前,它已经应用到抗洪抢险的预案生成和城市应急联动的决策支持上,效果表明它在预案生成速度以及实际可操作性上都具有明显优势.     

基于Agent和CBR的电子商务谈判系统模型研究*

提出一种融合了多Agent和案例推理（CBR）技术的电子商务谈判系统模型,在多Agent环境下应用CBR技术捕获并重用以前成功的谈判案例,从中提取适应性策略来为交易提供决策支持,这些策略可以根据所处环境的改变动态生成。对相关问题进行了讨论,包括谈判案例的匹配和谈判策略的选择。     

Two-step filtering datamining method integrating case-based reasoning and rule induction

Case-based reasoning (CBR) methods are applied to various target problems on the supposition that previous cases are sufficiently similar to current target problems, and the results of previous similar cases support the same result consistently. However, these assumptions are not applicable for some target cases. There are some target cases that have no sufficiently similar cases, or if they have, the results of these previous cases are inconsistent. That is, the appropriateness of CBR is different for each target case, even though they are problems in the same domain. Thus, applying CBR to whole datasets in a domain is not reasonable. This paper presents a new hybrid datamining technique called two-step filtering CBR and rule induction (TSFCR), which dynamically selects either CBR or RI for each target case, taking into consideration similarities and consistencies of previous cases. We apply this method to three medical diagnosis datasets and one credit analysis dataset in order to demonstrate that TSFCR outperforms the genuine CBR and RI.     

Mammographic case base applied for supporting image diagnosis of breast lesion

Mammography is an important screening tool for early detection of breast cancer. However, radiologists usually experience difficulties in image interpretation of grey zones. A computer system providing similar cases with known diagnostic results for decision support would be useful. Applying case-based reasoning (CBR) to a mammographic case base, constructed from prior cases with known diagnostic results, offers a solution to this problem. Serving as an inference tool, the CBR can retrieve similar cases to help radiologists interpret a new mammographic case. To evaluate the usability of this system, 34 licensed radiologists were invited as experts to assess the system. The results indicate that CBR applied to the mammographic case base is valuable for decision support in mammographic image interpretation.     

基于线索和改进最相邻近法的案例检索

基于线索和改进最相邻近法的案例检索算法是最相邻近法、归纳索引法和知识索引法的结合,是对案例匹配算法进行的一种探索。案例检索整体上采用最相邻近法的思想,局部相似度量采用归纳索引的思想,线索是检索过程中启发性知识和学习到知识的形式化描述,目的是提高案例检索的效率。     

Case-based reasoning for intelligent support of construction negotiation

Negotiation and conflict resolution are major ill-structured and complex problems in construction. Due to the uncertain and changing nature of the processes, it is important for negotiators to have access to previous records, to communicate effectively with each other, and to have an agent acting as the mediator when deadlocks occur. This paper presents a computer model which employs case-based reasoning (CBR) to provide intelligent support to construction negotiations. This model has been implemented in the MEDIATOR, a computer program that utilises previous cases as a basis for addressing new problems. In contrast to conventional expert systems (ESs) that use compiled knowledge in problem solving, the system selects similar cases to help in solving a given negotiation problem. The selected case is then modified and adapted to generate proposals that should move people towards a settlement.     

Global optimization of feature weights and the number of neighbors that combine in a case-based reasoning system

Abstract: Case-based reasoning (CBR) often shows significant promise for improving the effectiveness of complex and unstructured decision-making. Consequently, it has been applied to various problem-solving areas including manufacturing, finance and marketing. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still a challenging issue. Most previous studies on improving the effectiveness of CBR have focused on the similarity function aspect or optimization of case features and their weights. However, according to some of the prior research, finding the optimal k parameter for the k-nearest neighbor is also crucial for improving the performance of the CBR system. Nonetheless, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence techniques. In this study, we introduce a genetic algorithm to optimize the number of neighbors that combine, as well as the weight of each feature. The new model is applied to the real-world case of a major telecommunication company in Korea in order to build a prediction model for customer profitability level. Experimental results show that our genetic-algorithm-optimized CBR approach outperforms other artificial intelligence techniques for this multi-class classification problem.     

An interactive case-based reasoning method considering proximity from the cut-off point

Case-based reasoning (CBR) models often solve problems by retrieving multiple previous cases and integrating those results. However, conventional CBR makes decisions by comparing the integrated result with the cut-off point irrespective of the degree of the adjacency between them. This can cause increasing misclassification error for the target cases adjacent to the cut-off point, since the results of previous cases used to produce those results are relatively inconsistent with each other. In this article, we suggest a new interactive CBR model called grey-zone case-based reasoning (GCBR) that makes decisions focusing additional attention on the cases near the cut-off point by interactive communication with users. GCBR classifies results automatically for the cases placed outside the cut-off point boundary area. On the other hand, it communicates with users to make decision for the cases placed inside the area by verifying characteristics of the dataset. We suggest the architecture of GCBR and implement its prototype.     

基于范例推理的船舶避碰决策支持系统

根据船舶避碰的特点，将范例推理（Case-based Reasoning，CBR）方法引入到船舶避碰决策支持系统的设计中．为提高决策系统的性能，优化范例表示方法；并在分析阶段先对碰撞局面进行分类；再通过搜索树进行范例匹配；最后，讨论该方法的可行性．     

A recommender mechanism based on case-based reasoning

Case-based reasoning (CBR) algorithm is particularly suitable for solving ill-defined and unstructured decision-making problems in many different areas. The traditional CBR algorithm, however, is inappropriate to deal with complicated problems and therefore needs to be further revised. This study thus proposes a next-generation CBR (GCBR) model and algorithm. GCBR presents as a new problem-solving paradigm that is a case-based recommender mechanism for assisting decision making. GCBR can resolve decision-making problems by using hierarchical criteria architecture (HCA) problem representation which involves multiple decision objectives on each level of hierarchical, multiple-level decision criteria, thereby enables decision makers to identify problems more precisely. Additionally, the proposed GCBR can also provide decision makers with series of cases in support of these multiple decision-making stages. GCBR furthermore employs a genetic algorithm in its implementation in order to reduce the effort involved in case evaluation. This study found experimentally that using GCBR for making travel-planning recommendations involved approximately 80% effort than traditional CBR, and therefore concluded that GCBR should be the next generation of case-based reasoning algorithms and can be applied to actual case-based recommender mechanism implementation.     

Design and implementation of a case-based reasoning system for marketing plans

Unstructured intangible experiences and knowledge are usually difficult to represent and instantiate, which engenders the hardship of knowledge transfer and sharing. Past marketing plans are such valuable documents containing strategic planning knowledge and experiences.Case-Based Reasoning (CBR), which consists of retrieving, reusing, revising, and retaining cases, has been proved effective in retrieving information and knowledge from prior situations and being widely researched and applied in a great variety of problem territories.This paper targets at designing a CBR architecture and a method that facilitate the sharing and retrieving of cases of great concern to the marketing personnel. After an intensive survey of CBR methods and applications, a CBR system embedding multi-attribute decision making method, which provides both overall similarity level and similarity level of each selected attribute, is proposed to enhance the adaptation of a new marketing plan. In addition, a multi-attribute gap analysis diagram is developed to visualize the similarity along with the gap between candidate and target cases, so as to better support interaction and group decision making in the process of strategically formulating a new marketing plan. The CBR system was implemented and successfully demonstrated on case retrieval of a telecommunication company.     

A case-based classifier for hypertension detection

The exploration of three-dimensional (3D) anthropometry scanning data along with other existing subject medical profiles using data mining techniques becomes an important research issue for medical decision support. This research attempts to construct a classification approach based on the hybrid use of case-based reasoning (CBR) and genetic algorithms (GAs) for hypertension detection using anthropometric body surface scanning data. The obtained result reveals the relationship between a subject’s 3D scanning data and hypertension disease. The GA is adopted to determine the appropriate feature weights for CBR. The proposed approaches were experimented and compared with a regular CBR and other widely used approaches including neural nets and decision trees. The experiment showed that applying GA to determine the suitable weights in CBR is a feasible approach to improving the effectiveness of case matching of hypertension disease. It also demonstrated that different weighted CBR approach presents better classification accuracy over the results obtained from other approaches.     

Developing a diagnostic system through integration of fuzzy case-based reasoning and fuzzy ant colony system

This study intends to propose a hybrid Case-Based Reasoning (CBR) system with the integration of fuzzy sets theory and Ant System-based Clustering Algorithm (ASCA) in order to enhance the accuracy and speed in case matching. The cases in the case base are fuzzified in advance, and then grouped into several clusters by their own similarity with fuzzified ASCA. When a new case occurs, the system will find the closest group for the new case. Then the new case is matched using the fuzzy matching technique only by cases in the closest group. Through these two steps, if the number of cases is very large for the case base, the searching time will be dramatically saved. In the practical application, there is a diagnostic system for vehicle maintaining and repairing, and the results show a dramatic increase in searching efficiency.     

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.