• Journal of Ship and Ocean Technology
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• v.5 no.3
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• pp.27-35
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• 2001

In this research, hybrid method with case-based reasoning and rule-based reasoning is applied. Using case-based reasoning, design experts'experience and know-how are effectively represented in order to obtain a proper configuration of midship section in the initial ship design stage. Since there is not sufficient domain knowledge available to us, traditional case-adaptation algorithms cannot be applied to our problem, i.e., creating the configuration of midship section. Thus, new case-adaptation algorithms not requiring any domain knowledge are developed antral applied to our problem. Using the knowledge representation of DnV rules, rule-based reasoning can perform deductive inference in order to obtain the scantling of midship section efficiently. The results from the case-based reasoning and the rule-based reasoning are examined by comparing the results with various conventional methods. And the reasonability of our results is verified by comparing the results wish actual values from parent ship.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.97-112
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• 2000

The basic idea of case-based reasoning is to solve a new problem using the previous problem-solving experiences. In this research we develop a case-based reasoning system for equipment malfunction diagnosis. We first divide the case base into clusters using the case-based clustering technique. Then we develop an appropriate case-based diagnostic system for each cluster. In other words for individual cluster a different case-based diagnostic system which uses different weights for attributes is developed. As a result multiple case-based reasoning system are operating to solve a diagnostic problem. In comparison to the performance of the single case-based reasoning system our system reduces the computation time by 50% and increases the accuracy by 5% point.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.165-170
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• 2011

It is expected that the cooperation between rule-based reasoning and case-based reasoning gives us an efficient approach for flexible reasoning. In this paper, we present an integrated model of rule-base reasoning and case-base reasoning using the MVL automata model. In addition, we introduce how to handle the uncertainty in the integrated model.

• Asia pacific journal of information systems
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• v.14 no.3
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• pp.77-92
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• 2004

When humans make decisions, they differentiate classifications of individual attribute variables that affect the decisions according to the importance and pattern of each attribute variables. The present study examines the practicality of the proposed Code Arrangement-Based Reasoning(CABR), which resembles the human's way of reasoning. To this end, we developed a CABR technique that classifies each attribute variable affecting significant impacts on the target variable into a cluster and assigns a code to the cluster. For verifying the proposed technique, both case-based reasoning and CABR were used for the customer continuance judgment problem of an automobile insurance company. Results indicated that the performance of CABR is close to the one of the case-based reasoning. The CABR also shows the possibility of using bio-informatics techniques for organizational data analysis in the future.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.75-77
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• 1998

Perhaps one of the most versatile approaches to learning in practical domains lies in case based reasoning. To date, however, most case based reasoning systems have tended to focus on relatively simple domains. The current study involves the development of a decision support system for a complex production process with a limited database. This paper presents a set of critical issues underlying CBR, then explores their consequences for a complex domain. Finally, the performance of the system is examined for resolving various types of quality control problems.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.2
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• pp.221-228
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• 2020

This study suggests an alternative to the conventional collaborative filtering method for predicting consumer choice, using case-based reasoning. The algorithm of case-based reasoning determines the similarity between the alternative sets that each subject chooses. Case-based reasoning uses the inverse of the normalized Euclidian distance as a similarity measurement. This normalized distance is calculated by the ratio of difference between each attribute level relative to the maximum range between the lowest and highest level. The alternative case-based reasoning based on similarity predicts a target subject's choice by applying the utility values of the subjects most similar to the target subject to calculate the utility of the profiles that the target subject chooses. This approach assumes that subjects who deliberate in a similar alternative set may have similar preferences for each attribute level in decision making. The result shows the similarity between comparable alternatives the consumers consider buying is a significant factor to predict the consumer choice. Also the interaction effect has a positive influence on the predictive accuracy. This implies the consumers who looked into the same alternatives can probably pick up the same product at the end. The suggested alternative requires fewer predictors than conjoint analysis for predicting customer choices.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.203-206
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• 1996

Prediction of firm bankruptcy have been extensively studied in accounting, as all stockholders in a firm have a vested interest in monitoring its financial performance. The objective of this paper is to develop the hybrid models for bankruptcy prediction. The proposed hybrid models are two phase. Phase one are (a) DA-assisted neural network, (b) Logit-assisted neural network, and (c) Genetic-assisted neural network. And, phase two are (a) DA-assisted Case based reasoning, and (b) Genetic-assisted Case based reasoning. In the variables selection, We are focusing on three alternative methods - linear discriminant analysis, logit analysis and genetic algorithms - that can be used empirically select predictors for hybrid model in bankruptcy prediction. Empirical results using Korean medium-sized firms data show that hybrid models are very promising neural network models and case based reasoning for bankruptcy prediction in terms of predictive accuracy and adaptability.

• Journal of the Korean Society of Safety
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• v.14 no.1
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• pp.167-176
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• 1999

Construction accidents occur reiteratively in similar fashions. There have been several attempts to develop a safety program for preventing construction accidents on sites. It will be very effective to use previous accident cases for establishing proper safety plan and managing safety process. This research develops a case-based construction safety management system which enables construction managers or safety managers to prevent potential accidents during the construction process. The case-oriented approach is performed through the representation of previous accident cases in accordant with the similarity to the conditions of current site. It uses a case-based reasoning which is one of the reasoning methods of an expert system. A prototype system for the reasoning model was implemented using one of the case based system development tools. The system was applied to a real construction site to verify its capability and validity. It was founded that the causes of accidents were successfully removed, so the proposed model proved to be reasonable. Additional research is needed to resolve the technical problem how to adapt the countermeasures for accident prevention provided by the reasoning model.

• Asia pacific journal of information systems
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• v.25 no.4
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• pp.626-641
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• 2015

Conventional case-based reasoning (CBR) does not perform efficiently for high-volume datasets because of case retrieval time. To overcome this problem, previous research suggested clustering a case base into several small groups and retrieving neighbors within a corresponding group to a target case. However, this approach generally produces less accurate predictive performance than the conventional CBR. This paper proposes a new case-based reasoning method called the clustering-merging CBR (CM-CBR). The CM-CBR method dynamically indexes a search pool to retrieve neighbors considering the distance between a target case and the centroid of a corresponding cluster. This method is applied to three real-life medical datasets. Results show that the proposed CM-CBR method produces similar or better predictive performance than the conventional CBR and clustering-CBR methods in numerous cases with significantly less computational cost.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.237-240
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• 2005

This paper describer a design expert system which determines the design values of shadow mask using Case-Based Reasoning. In Case-Based Reasoning, it is important to both retrieve similar cases and adapt the cases to meet the design specifications exactly. Especially, the difficulty in automating the adaptation process will prevent the designers from using the design expert systems efficiently and easily. This paper explains knowledge-based design support systems for shadow mask through neural network-based case adaptation. Specifically, we developed 1) representing design knowledge and 2) adaptive case-based reasoning method using RBFN (Radial Basis Function Network).