Case-Based Reasoning Cost Estimation Model Using Two-Step Retrieval Method

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.

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.

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.

Case Based Reasoning (CBR) is a method that aims to resolve a new case by adapting the solutions contained in previous cases that are similar to the new case. The system built in this study is the CBR system to make recommendations on the topic of student thesis concentration. This study used data from undergraduate students of Informatics Engineering IST AKPRIND Yogyakarta with a total of 115 data consisting of 80 training data and 35 test data. This study aims to design and build a Case Based Reasoning system using the Nearest Neighbor and Manhattan Distance Similarity Methods, and to compare the results of the accuracy value using the Nearest Neighbor Similarity and Manhattan Distance Similarity methods. The recommendation process is carried out by calculating the value of closeness or similarity between new cases and old cases stored on a case basis using the Nearest Neighbor Method and Manhattan Distance. The features used in this study consisted of GPA and course grades. The case taken is the case with the highest similarity value. If a case doesnt get a topic recommendation or is less than the trashold value of 0.8, a case revision will be carried out by an expert. Successfully revised cases are stored in the system to be made new knowledge. The test results using the Nearest Neighbor Method get an accuracy value of 97.14% and Manhattan Distance Method 94.29%.

Many studies on land use change (LUC), using different approaches and models, have yielded good results. Applications of these methods have revealed both advantages and limitations. However, LUC is a complex problem due to influences of many factors, and variations in policy and natural conditions. Hence, the characteristics and regional suitability of different methods require further research, and comparison of typical approaches is required. Since the late 1980s, CA has been used to simulate urban growth, urban sprawl and land use evolution successfully. Nowadays it is very popular in resolving the LUC estimating problem. Case-based reasoning (CBR), as an artificial intelligence technology, has also been employed to study LUC by some researchers since the 2000s. More and more researchers used the CBR method in the study of LUC. The CA approach is a mathematical system constructed from many typical simple components, which together are capable of simulating complex behavior, while CBR is a problem-oriented analysis method to solve geographic problems, particularly when the driving mechanisms of geographic processes are not yet understood fully. These two methods were completely different in the LUC research. Thus, in this paper, based on the enhanced CBR model, which is proposed in our previous research (Du et al. 2009), a comparison between the CBR and CA approaches to assessing LUC is presented. LUC in Dongguan coastal region, China is investigated. Applications of the improved CBR and the cellular automata (CA) to the study area, produce results demonstrating a similarity estimation accuracy of 89% from the improved CBR, and 70.7% accuracy from the CA. From the results, we can see that the accuracies of the CA and CBR approaches are both >70%. Although CA method has the distinct advantage in predicting the urban type, CBR method has the obvious tendency in predicting non-urban type. Considering the entire analytical process, the preprocessing workload in CBR is less than that of the CA approach. As such, it could be concluded that the CBR approach is more flexible and practically useful than the CA approach for estimating land use change.

In the world of electricity, transformers are widely used, both in the field of electric power and electronics. The use of transformers in the electric power system is used for each of its needs, for example the need for high voltage in the delivery of electrical power over long distances. Electrical equipment transformers are expensive and are very vital equipment. If the transformer is disturbed or in an abnormal condition, there may be an unexpected temporary cessation of electricity distribution and will cause losses for PLN and consumers who use electricity by disrupting the activities carried out. The Case Based Reasoning (CBR) method is a case-based reasoning system that uses previous experiences or cases so that they can solve new problems or cases. There are several stages in the case based reasoning method, including retrieve, reuse, revise, and retain. Case-based Reasoning (CBR) collects previous cases that are similar to the new problem and tries to modify the solution to fit the new case. The basic idea of ​​Case-Based reasoning is the assumption that similar problems have similar solutions. While this assumption is not always true, it does depend on many practical domains. Where the previous case experienced an overvoltage transformer damage, using the CBR method will look for similarities to the previous case with existing characteristics, the CBR method will look for the same case on the existing Knowladge so that the damage is quickly detected. From the characteristics of the damage that is on the transformer, it is easy to diagnose transformer damage using the CBR method and the results of the CBR method will find a ranking that is almost similar to the existing case.

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.

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.

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.

Case retrieval is the focal stage in a Case-Based Reasoning (CBR) system. In this paper, a new case retrieval method called CRGCU is proposed in a systematical way, which is based on Genetic Algorithms (GAs) and Group Decision-Making method. First, feature attribute weights of cases are optimized by using GAs. Second, instead of utilizing only one set of feature attribute weights, we calculate similarity based on multiple sets of optimized weights. The result of case retrieval can be obtained at last by adopting Group Cardinal Utility function. The experiments on both static and dynamic data sets illustrate that our method is effective and suitable for CBR system.

Conceptual cost estimation is an important step in project feasibility decisions when there is not enough information on detailed design and project requirements. Methods that enable quick and reasonably accurate conceptual cost estimates are crucial for achieving successful decisions in the early stages of construction projects. For this reason, numerous machine learning methods proposed in the literature that use different learning mechanisms. In recent years, the case-based reasoning (CBR) method has received particular attention in the literature for conceptual cost estimation of construction projects that use similarity-based learning principles. Despite the fact that CBR provides a powerful and practical alternative for conceptual cost estimation, one of the main criticisms about CBR is its low prediction performance when there is not a sufficient number of cases. This paper presents a bootstrap aggregated CBR method for achieving advancement in CBR research, particularly for conceptual cost estimation of construction projects when a limited number of training cases are available. The proposed learning method is designed so that CBR can learn from a diverse set of training data even when there are not a sufficient number of cases. The performance of the proposed bootstrap aggregated CBR method is evaluated using three data sets. The results revealed that the prediction performance of the new bootstrap aggregated CBR method is better than the prediction performance of the existing CBR method. Since the majority of conceptual cost estimates are made with a limited number of cases, the proposed method provides a contribution to CBR research and practice by improving the existing methods for conceptual cost estimating.

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.

Hydrocephalus is a medical condition characterized by the accumulation of cerebrospinal fluid in the brain, which can lead to increased intracranial pressure and neurological disturbances. Early and accurate diagnosis is crucial for medical professionals to determine the type of hydrocephalus the patient has and provide the appropriate solution. This study aims to develop a Case-Based Reasoning (CBR) system to assist healthcare providers in diagnosing hydrocephalus more effectively. With this system, medical professionals can identify the type of hydrocephalus based on the symptoms experienced by the patient and receive recommendations based on previous cases. The research method used in this study is Case-Based Reasoning (CBR), an experience-based approach that relies on documented cases to solve new problems by comparing them to similar past cases. Data for this study were obtained from Andalas University Hospital and consultations with experts. The dataset used includes 19 hydrocephalus symptom data points and two main classifications of hydrocephalus: congenital hydrocephalus and acquired hydrocephalus. The results show that the CBR method has an accuracy rate of 63.15% in diagnosing congenital hydrocephalus and 44% in diagnosing acquired hydrocephalus. Although the accuracy rate still requires improvement, this study demonstrates that the CBR method has significant potential as a tool to assist in diagnosing hydrocephalus. The system can provide analysis based on historical data and help healthcare providers make more informed decisions. This study emphasizes that CBR can be an alternative to expert systems in hydrocephalus diagnosis. However, to improve accuracy and reliability in clinical practice, further testing with a larger dataset and integration with other artificial intelligence methods is needed. With further development, this system has the potential to become a more precise diagnostic tool and be more widely implemented in the medical field.

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

Case-based reasoning (CBR) infers a solution to a new problem by searching a collection of previously solved problems for cases which are similar to the new problem. The collection of previous problems and their associated solutions represents the CBR system's realm of expertise. A CBR system helps to exploit data so that smarter decisions can be made in less time and/or at lower cost. A key issue is that can we always trust the solutions suggested by a case-based reasoning system? This paper studies the reliability of CBR systems based on previous study results, factors affecting the reliability of a CBR system are also discussed in this paper, especially the property that whether inter-feature of case exists redundancy. After that, the reliability of an individual suggested solution is studied. To illustrate these ideas, some experiments and their results are discussed in this paper. The results of experiments show a new route concerning on how to improve the reliability of a CBR system at an overall level.