Empirical evaluation of retrieval in case-based reasoning systemsusing modified cosine matching function

Case-based reasoning (CBR) supports ill-structured decision making by retrieving previous cases that are useful toward the solution of a new decision problem. The usefulness of previous cases is determined by assessing the similarity of a new case with the previous cases. In this paper, we present a modified form of the cosine matching function that makes it possible to contrast the two cases being matched and to include differences in the importance of features in the new case and the importance of features in the previous case. Our empirical evaluation of a CBR application to a diagnosis and repair task in an electromechanical domain shows that the proposed modified cosine matching function has a superior retrieval performance when compared to the performance of nearest-neighbor and the Tversky's contrast matching functions     

An integrated case-based reasoning approach for intelligent help desk fault management

As modern business functions become more complex and knowledge-intensive, with increasing demands for quality services, there is an emerging trend for organisations to develop and deploy intelligent knowledge-based systems for mission-critical operations. Some of the challenges in successfully implementing this breed of systems depend on how well the intelligent system is integrated with conventional existing information systems and workflow, and the quality of the intelligent system itself. Developing quality expert systems lies in the effective modelling of cognitive processes of human experts and representation of various forms of related knowledge in a domain. An integrated intelligent system called the Intelligent Help Desk Facilitator (IHDF), has been developed for computer and network fault management. The system, which comprises various modules including an expert system, is successfully deployed in a problem response help desk environment of a local bank. This paper describes a cognitive-driven approach to the development of the expert system based on a hybrid knowledge representation and reasoning strategy. The approach incorporates a hybrid case-based reasoning (CBR) framework of techniques which include case memory organisation structures (discrimination networks and shared-featured networks), case indexing and retrieval schemes (fuzzy character-matching, nearest-neighbour similarity matching and knowledge-guided indexing); and an interactive and incremental style of reasoning. The paper discusses the design and implementation of the expert system component of IHDF and illustrates the appropriateness of the hybrid architecture for problem resolution and diagnostic types of applications.     

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。     

Case-based reasoning and neural network based expert system for personalization

We suggest a hybrid expert system of case-based reasoning (CBR) and neural network (NN) for symbolic domain. In previous research, we proposed a hybrid system of memory and neural network based learning. In the system, the feature weights are extracted from the trained neural network, and used to improve retrieval accuracy of case-based reasoning. However, this system has worked best in domains in which all features had numeric values. When the feature values are symbolic, nearest neighbor methods typically resort to much simpler metrics, such as counting the features that match. A more sophisticated treatment of the feature space is required in symbolic domains.We propose feature-weighted CBR with neural network, which uses value difference metric (VDM) as distance function for symbolic features. In our system, the feature weight set calculated from the trained neural network plays the core role in connecting both the learning strategies. Moreover, the explanation on prediction can be given by presenting the most similar cases from the case base. To validate our system, illustrative experimental results are presented. We use datasets from the UCI machine learning archive for experiments. Finally, we present an application with a personalized counseling system for cosmetic industry whose questionnaires have symbolic features. Feature-weighted CBR with neural network predicts the five elements, which show customers’ character and physical constitution, with relatively high accuracy and expert system for personalization recommends personalized make-up style, color, life style and products.     

Fuzzy rule-based similarity model enables learning from small case bases

The concept of similarity plays a fundamental role in case-based reasoning. However, the meaning of “similarity” can vary in situations and is largely domain dependent. This paper proposes a novel similarity model consisting of linguistic fuzzy rules as the knowledge container. We believe that fuzzy rules representation offers a more flexible means to express the knowledge and criteria for similarity assessment than traditional similarity metrics. The learning of fuzzy similarity rules is performed by exploiting the case base, which is utilized as a valuable resource with hidden knowledge for similarity learning. A sample of similarity is created from a pair of known cases in which the vicinity of case solutions reveals the similarity of case problems. We do pair-wise comparisons of cases in the case base to derive adequate training examples for learning fuzzy similarity rules. The empirical studies have demonstrated that the proposed approach is capable of discovering fuzzy similarity knowledge from a rather low number of cases, giving rise to the competence of CBR systems to work on a small case library.     

A fuzzy case based reasoning approach to value engineering

This paper is intended to assist the experts during the creativity phase of value engineering through utilizing the past experiences and avoid them in a specific domain from repeating the same experience. To this purpose, a general fuzzy case based reasoning (CBR) system is developed. Our system benefits from a fuzzy clustering model for fuzzy data to facilitate case retrieval and reduce the time complexity. The inherent analogical nature of a case-based reasoning (CBR) model and its integration with fuzzy theory would facilitate access to more precise and systematically classified information during a VE workshop. In order to test the performance of the proposed system, it is applied to suburban highway design data extracted from National Cooperative Highway Research Program (NCHRP) Report 282.     

Predicting financial activity with evolutionary fuzzy case-based reasoning

Predicting financial activity through examining the short-term liquidity is crucial within today’s turbulent financial environment. Firms, governments, and individuals all need an effective methodology based on liquidity information that plays performance deterioration warning a priori bankruptcy prediction. In this paper, we propose a hybrid decision model using case-based reasoning augmented with genetic algorithms (GAs) and the fuzzy k nearest neighbor (fuzzy k-NN) methods for predicting the financial activity rate. GAs are used to determine the optimal or near-optimal weight vector of financial features expressed in linguistic values by the expert. A fuzzy k-NN-based CBR scheme is designed to compute memberships of financial activity rates and to provide a more flexible and practical mechanism for acquiring, creating, and reusing the expert’s decision knowledge. An empirical experimentation using 746 publicly traded Taiwanese firms shows that the average accuracy of the rating is about 92.36%, which is superior to other related models. The proposed approach not only can lend support to the decision of an expert, but also allow proper feedback for the expert to improve the quality of the decision.     

Interactive Case-Based Reasoning in Sequential Diagnosis

Interactive trouble-shooting and customer help-desk support, both activities that involve sequential diagnosis, represent the majority of applications of case-based reasoning (CBR). An analysis is presented of the user-interface requirements of intelligent systems for sequential diagnosis. We argue that mixed-initiative dialogue, explanation of reasoning, and sensitivity analysis are essential to meet the needs of experienced as well as novice users. Other issues to be addressed by system designers include relevance and consistency in dialogue, tolerance of missing data, and timely provision of feedback to users. Many of these issues have previously been addressed by the developers of expert systems and the lessons learned may have important implications for CBR. We present a prototype environment for interactive CBR in sequential diagnosis, called CBR Strategist, which is designed to meet the identified requirements.     

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 fuzzy expert database system

Fuzzy concepts always exist in much of human reasoning as well as decision making. This paper presents a fuzzy expert database system which is an integration of a fuzzy expert system building tool called SYSTEM Z-II and a database management system called Rdb/VMS. This system is able to extract fuzzy data and terms stored in a database and used in the fuzzy reasoning in an expert system. It can also retrieve information by fuzzy database-queries which are generated by the expert system automatically. Many expert systems in different domain areas such as decision making can be constructed. Sample applications are described to demonstrate the flexibility and power of this system. The fuzzy query language defined and used in the system can also be used independently as a fuzzy enquiry tool in database applications.     

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。     

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.     

A case-based procurement advisory system for construction

The selection and use of an appropriate procurement system are fundamental to the success of a construction project. However, the procurement selection process involves the analysis of complex and dynamic criteria such as cost certainty, time certainty, speed, flexibility, etc. Procurement selection is, therefore, plagued with uncertainty and vagueness that is difficult to be represented by a generalized set of rules. In reality, decisions in procurement selection are usually derived from intuition and past experience. Case-based reasoning (CBR) appears to be an appropriate approach to meet the requirements of the procurement selection process because of the value of experiential knowledge. This paper reviews the practicality and suitability of a CBR approach for procurement selection through the development of a prototype case-based procurement advisory system. In this prototype system, procurement selection cases are represented by a set of attributes elicited from experienced procurement experts. The system is powered by a fuzzy similarity retrieval mechanism, which gives a greater accuracy than the normal similarity retrieval process. The results indicate that the CBR approach can suitably model the characteristics of construction procurement selection, and provide an indication of potential outcomes to any apparently suitable procurement methods.     

Learning fuzzy rules for similarity assessment in case-based reasoning

Fundamental to case-based reasoning is the assumption that similar problems have similar solutions. The meaning of the concept of “similarity” can vary in different situations and remains an issue. This paper proposes a novel similarity model consisting of fuzzy rules to represent the semantics and evaluation criteria for similarity. We believe that fuzzy if-then rules present a more powerful and flexible means to capture domain knowledge for utility oriented similarity modeling than traditional similarity measures based on feature weighting. Fuzzy rule-based reasoning is utilized as a case matching mechanism to determine whether and to which extent a known case in the case library is similar to a given problem in query. Further, we explain that such fuzzy rules for similarity assessment can be learned from the case library using genetic algorithms. The key to this is pair-wise comparisons of cases with known solutions in the case library such that sufficient training samples can be derived for genetic-based fuzzy rule learning. The evaluations conducted have shown the superiority of the proposed method in similarity modeling over traditional schemes as well as the feasibility of learning fuzzy similarity rules from a rather small case base while still yielding competent system performance.     

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。