Case Retrieval Strategy Based on GAs and Group Decision-Making Method

The distribution of the case feature attribute weights directly affects the case retrieval result. Aim at improving the retrieval precision, a retrieval method is proposed based on group decision-making for optimizing the case feature attributes weights. Firstly, multiple groups of initial weights are obtained by genetic algorithm. Then, the multiple sets of retrieval results produced by these weights are optimized through group decision-making method, and the weights can be dynamic adjusted through the deviations between individual decision results and group result. The simulation results indicate that the proposed approach can fully excavate the potential knowledge of attribute weights and thus result in higher retrieval accuracy in a case-based reasoning system. The PID adjusting comparison experiment of typical two order delay system verifies the effectiveness of the new method.

Case-based reasoning (CBR) is an artificial intelligence (AI) technique for solving problems. The very fact that CBR draws on past experiences to solve a new problem makes it intuitively appealing as humans also have the same problem-solving behavior. Another advantage of CBR over other conventional AI techniques is that it can work on a shallow knowledge base to start with. These make CBR an excellent method to solve real-life problems and useful in the field like medical diagnosis, engineering diagnosis, product selection, weather prediction, aerospace applications, etc. Classification plays a vital role in the retrieval of cases, as a correct classification results in a correctly retrieved case, which eventually results in a correct solution given by the CBR system. Typically, case retrieval is similarity-based and uses a k-nearest neighbor (k-NN) algorithm. Retrieval aims to find among the stored cases the best match for a given new case. Typically, CBR systems use the nearest neighbor algorithm as a similarity metric for retrieving cases. In this paper, the researchers use the machine learning workbench WEKA to combine well-known classifiers multilayer perceptron and fuzzy-rough nearest neighbor and compare the performance of k-NN with them. They have used benchmark medical data sets to carry out the evaluation process. The experimental results show that the combination of multilayer perceptron and fuzzy-rough nearest neighbor outperforms k-NN to a significant extent for classification, thus effectively improving the case retrieval efficiency and performance.

In this paper, we propose a hybrid case-based reasoning (CBR) system for predicting the construction cost of high-rise buildings at the preliminary design stage. First, the extracted cost factors (CFs) of a high-rise building were shown to significantly improve the cost estimation system’s performance. For developing a CBR system, a hybrid approach that combines CBR with genetic algorithms (GAs) for cost estimation was adopted. Genetic algorithms were used for optimized weight generation and applied to real project cases. Additionally, this paper proposes the identification of an alternative similarity score measurement formula. The proposed formula evaluates the contrast between the alternative case matching approach and the classical formula in a scenario involving the use of cost factors describing a case. The results indicate that the proposed GA-based CBR system can consistently reduce errors and potentially be useful to owners and contractors in the early financial planning stage. Accordingly, it is expected that the developed CBR system would provide decision-makers with accurate cost information to assess and compare multiple alternatives for obtaining the optimal solution and controlling the cost.

Similar case retrieval ability is a key technology in CBR(Case Based Reasoning) system. In order to improve the case retrieval efficiency in business financial distress warning(FDW) system, a CBR case retrieval model based on gray relation was proposed, applying the gray relational analysis in case based reasoning for business FDW which improved the deficiency of distance measurement. Moreover, taking into account the different importance of case features in predicting financial distress, a weight vector was defined to solve the influence coming from nor-crucial features. Empirical results proves that this method can effectively improve the case retrieval efficiency of the target enterprise in business FDW system.

This chapter is the second chapter in Part II of the book. It is also the basis for Chapter 9, as shown in the shaded area of Fig. 7.1. This chapter first examines the relationship between case-based reasoning (CBR) systems and multiagent systems (MASs), and proposes knowledge-based models of multiagent CBR systems from both logical and knowledge-based viewpoints. Then this chapter investigates the case base and case retrieval in a distributed setting and examines the integration of case-based reasoning capabilities in a BDI architecture. This chapter also discusses CBR for agent team cooperation. Finally this chapter proposes an agent architecture using CBR to model an agent negotiation strategy.

A case-based reasoning system for supervised classification problems in the medical field

Medical case-based reasoning (CBR) systems require the handling of vague or imprecise data. The fuzzy set theory is particularly suitable for this purpose. This paper proposes a case-base preparation framework for CBR systems, which converts the electronic health record medical data into fuzzy CBR knowledge. It generates fuzzy case-base knowledge by suggesting a standard crisp entity–relationship data model for CBR case-base. The resulting data model is fuzzified using a proposed relational data model fuzzification methodology. The performances of this methodology and its resulting fuzzy case-base structure are evaluated. Diabetes diagnosis is used as a case study. A set of 60 real diabetic cases is used in the study. A fuzzy CBR system is implemented to check the diagnoses accuracy. It combines the resulting fuzzy case-base with a proposed fuzzy similarity measure. Experimental results indicate that the proposed fuzzy CBR method is superior to traditional CBR and other machine-learning methods. Our fuzzy CBR achieves an accuracy of 95%, a precision of 96%, a recall 97.96%, an f-measure of 96.97%, a specificity of 81.82%, and good robustness for dealing with vagueness. The resulting fuzzy case-base relational database enhances the representation of case-base knowledge, the performance of retrieval algorithms, and the querying capabilities of CBR systems.

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.

Case-based reasoning (CBR) method can make estimators understand the estimation process more clearly. Thus, CBR is widely used as a methodology for cost estimation. In CBR, the quality of case retrieval affects the relevance of retrieved cases and hence the overall quality of the reminding capability of CBR system. Thus, it is essential to retrieve relevant past cases for establishing a robust CBR system. Case retrieval needs the following tasks to obtain appropriate case(s); indexing, search, and matching (Aamodt and Plaza 1994). However, the previous CBR researches mostly deal with matching process that has limits such as accuracy and efficiency of case retrieval. In order to address this issue, this research presents a CBR cost model for building projects that has two-step retrieval process: decision tree and nearest neighbor methods. Specifically, the proposed cost model has indexing, search and matching modules. Features in the model are divided into shape-based and scale-based attributes. Based on these, decision tree is established for facilitating the search task and nearest neighbor method was utilized for matching task. In regard to applying nearest neighbor method, attribute weights are assigned using GA optimization and similarity is calculated using the principle of distance measuring. Thereafter, the proposed CBR cost model is developed using 174 cases and validated using 12 test cases.

By design, Case-Based Reasoning (CBR) systems do not need deep general knowledge. In contrast to (rule-based) expert systems, CBR systems can already be used with just some initial knowledge. Further knowledge can then be added manually or learned over time. CBR systems are not addressing a special group of users. Expert systems, on the other hand, are intended to solve problems similar to human experts. Because of the complexity and difficulty of building and using expert systems, research in this area addressed generating explanations right from the beginning. But for knowledge-intensive CBR applications, the demand for explanations is also growing. This paper is a first pass on examining issues concerning explanations produced by CBR systems from the knowledge containers perspective. It discusses what naturally can be explained by each of the four knowledge containers (vocabulary, similarity measures, adaptation knowledge, and case base) in relation to scientific, conceptual, and cognitive explanations.

To improve the performance of electronic commerce recommendation system (ECRS), the system implement frame by case-based reasoning (CBR) and artificial immune system (AIS) is proposed. The inner mechanism of AIS is analyzed, in leaning subsystem of ECRS, the AIS implement cycles such as antigen identifying, initial antibody population generating, affinity calculating, clone selecting, population updating and so on are studied. The key techniques of CBR for reasoning subsystem of ECRS such as case representation, case retrieval, case adaptation and case maintenance are introduced. The ECRS is tested by history record information of some Website, the result demonstrates that both the recommendation accuracy and reasoning response velocity are improved, the ECRS frame based on CBR and AIS is a potent one.

Several artificial intelligence approaches, particularly case-based reasoning (CBR), which is analogous to the context of human reasoning for problem resolution, have demonstrated their efficiency and reliability in the medical field. In recent years, deep learning represents the latest iteration of an advance in artificial intelligence technologies in medicine to aid in data classification, diagnosis of new diseases, and complex decision-making. Although these two independent approaches have good results in the medical field, the latter is still a complex field. This chapter reviews the available literature on CBR systems, deep learning systems, and CBR deep learning systems in medicine. The methods used and results obtained are discussed, and key findings are highlighted. Further, in the light of this review, some directions for future research are given. This chapter presents the proposed approach, which helps to make the retrieval phase of the CBR cycle more reliable and robust.

The retrieval-only Case-Based Reasoning (CBR) systems do not provide acceptable accuracy in critical domains such as medical. Besides, the case adaptation process in CBR is often a challenging issue as it has been traditionally carried out manually by domain experts. In this paper, a new case-based approach using transformational adaptation rules called "range adaptation rules" is proposed to improve the accuracy of a retrieval-only CBR system. The rangeadaptation rules are automatically generated from the case-base. In this approach, after solving each new problem, the case-base is expanded and the range adaptation rules are updated automatically. To evaluate the proposed approach, a prototype is implemented and experimented in agriculture domain to classify the IRIS plant types. The experimental results show that the proposed approach increases the classification accuracy comparing with the retrieval-only CBR system.KeywordsCase-based reasoning (CBR)Case adaptationIRIS plant classificationRange adaptation rules

Academic advising is a repetitive, time-consuming, and tedious job. In providing academic recommendations, an academic advisor applies three forms of knowledge i.e. formal knowledge, and Formal knowledge is the knowledge about the curriculum as given in the university and department catalogs. The expert knowledge is gained by training and experience. The case-based knowledge is obtained from the accumulated academic cases in the department. The best choice to eliminate the disadvantages of manual academic advising is to automate it. The key to automation lies in the effective application of the computer science field called artificial intelligence (AI).It was therefore considered by the Mechanical Engineering Department at the University of Queensland that a new framework for an Academic Advisory System (AAS) should be developed as the topic of this PhD. This thesis also develops and demonstrates a prototype based on the new framework. The AAS is an integrated system consisting of an Academic Database Management System (ADBMS), an Expert System (ES) based on decision tables, and a Case Based Reasoning System (CBRS). The ES deals with adviser's formal knowledge and expert knowledge, while the CBRS deals with adviser's case-based knowledge. The ADBMS is used to manage large and diverse academic data such as schedules, time tables, and hundreds of students' records and also to prepare input data for the ES and the CBRS.A new framework of the development of a Case Based Reasoning System (CBRS) using the relational model and extending the case based reasoning techniques by combining CBR with GT coding system, fuzzy sets, and rule base has been developed. GT coding system plays the main role as the foundation of the knowledge representation. Fuzzy sets contribute by making the case retrieval more flexible; and rules provide an easy strategy for case adaptation.To test the approach, prototypes of the AAS and the CBRS have been developed. They are PC based and run under Microsoft Windows and were developed using the relational database packages Paradox for Windows and Microsoft Access. The prototypes demonstrates the general techniques needed for academic advising and has been tested to the specific application of advice within the Department of Mechanical Engineering, at The University of Queensland, Australia and the Department of Mechanical Engineering, at The University of Brawijaya, Indonesia.

This paper shows how humans and machines can better collaborate to acquire adaptation knowledge (AK) in the framework of a case-based reasoning (CBR) system whose knowledge is encoded in a semantic wiki. Automatic processes like the CBR reasoning process itself, or specific tools for acquiring AK are integrated as wiki extensions. These tools and processes are combined on purpose to collect AK. Users are at the center of our approach, as they are in a classical wiki, but they will now benefit from automatic tools for helping them to feed the wiki. In particular, the CBR system, which is currently only a consumer for the knowledge encoded in the semantic wiki, will also be used for producing knowledge for the wiki. A use case in the domain of cooking is given to exemplify the man-machine collaboration.