• Journal of Ship and Ocean Technology
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• 제5권3호
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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.

• Asia pacific journal of information systems
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• 제25권4호
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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.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2005년도 추계학술대회 및 정기총회
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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).

• 경영정보학연구
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• 제16권1호
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• pp.37-50
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• 2014

사례기반 추론기법(case-based reasoning)은 수많은 데이터 속에서 현재 문제와 유사한 과거데이터를 실시간으로 탐색하고 복원해내야 하기 때문에, 과거에 축적된 데이터의 양이 방대하거나 또는 데이터의 축적 속도가 빠를 경우 계산비용(computational cost)이 급격히 높아지는 확장성(scalability) 문제를 갖는다. 이러한 문제를 해결하기 위하여, 기존의 일부 연구들은 클러스터링(clustering) 기법을 적용하여, 전체 데이타를 사전에 몇 개의 그룹으로 분류한 후, 특정 클러스터 내에서만 과거 사례를 탐색하도록 하는 클러스터링과 사례기반 추론의 하이브리드 기법을 제안하였다. 그러나 이러한 기법은 클러스터 수를 얼마로 설정했는지에 따른 성능편차가 심하고, 또한 기본적인 사례기반 추론기법에 비해 일반적으로 낮은 예측성능을 도출하는 문제점이 있다. 본 연구는 이러한 기존의 클러스터-사례기반추론기법의 문제점을 실증적으로 분석하고, 이를 극복할 수 있는 새로운 하이브리드(hybrid) 사례기반 추론기법을 제안한다. 제안된 기법은 실제 심장병환자를 예측하는 문제에 적용하였으며, 그 결과 제안된 기법이 기존의 사례기반 추론기법에 비해 현격하게 낮은 계산비용을 사용하면서도, 유사한 수준의 예측성능을 도출할 수 있음을 확인하였다.

• The Journal of Asian Finance, Economics and Business
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• 제7권2호
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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.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문초록집
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• pp.382.1-382
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• 2002

This paper presents a new approach integrated Case-Based Reasoning with Self- Organization Map(SOM) in diagnosis systems. The causes of faults are obtained by case-base trained from SOM. When the vibration problem of rotating machinery occurs, this provides an exact diagnosis method that shows the fault cause of vibration problem. In order to verify the performance of algorithm, we applied it to diagnose the fault cause of the electric motor.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1997년도 추계학술대회발표논문집; 홍익대학교, 서울; 1 Nov. 1997
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• pp.105-108
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• 1997

Previous studies in stock market predictions using artificial intelligence techniques such as artificial neural networks and case-based reasoning, have focused mainly on spot market prediction. Korea launched trading in index futures market (KOSPI 200) on May 3, 1996, then more people became attracted to this market. Thus, this research intends to predict the daily up/down fluctuant direction of the price for KOSPI 200 index futures to meet this recent surge of interest. The forecasting methodologies employed in this research are the integration of genetic algorithm and artificial neural network (GAANN) and the integration of genetic algorithm and case-based reasoning (GACBR). Genetic algorithm was mainly used to select relevant input variables. This study adopts the categorical data preprocessing based on expert's knowledge as well as traditional data preprocessing. The experimental results of each forecasting method with each data preprocessing method are compared and statistically tested. Artificial neural network and case-based reasoning methods with best performance are integrated. Out-of-the Model Integration and In-Model Integration are presented as the integration methodology. The research outcomes are as follows; First, genetic algorithms are useful and effective method to select input variables for Al techniques. Second, the results of the experiment with categorical data preprocessing significantly outperform that with traditional data preprocessing in forecasting up/down fluctuant direction of index futures price. Third, the integration of genetic algorithm and case-based reasoning (GACBR) outperforms the integration of genetic algorithm and artificial neural network (GAANN). Forth, the integration of genetic algorithm, case-based reasoning and artificial neural network (GAANN-GACBR, GACBRNN and GANNCBR) provide worse results than GACBR.

• 국제학술발표논문집
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• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
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• pp.182-186
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• 2011

Early cost estimates are important to decision-making for a construction project. Moreover, the possibility of reducing the project cost is getting less as the project is progressed. Case-based reasoning (CBR), which can be viewed as an effective method for early cost estimating, is widely utilized recently. Early cost estimates using CBR have advantages over the traditional ones as they produce reasonable outputs and self-studying is possible by simply adding new cases. Case-based reasoning is composed of a cycle of retrieve, reuse, revise, and retain process. However, in the majority of research cases, they are focused on how to retrieve the similar cases, instead of revising the cases which is expected to increase accuracy results of cost estimation. This research suggests a method of revising retrieved similar cases in a GA-CBR cost model which is widely studied and utilized for early cost estimating recently. To validate the proposed method, case study is conducted based on Korean public apartment projects.

• Spatial Information Research
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• 제9권2호
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• pp.171-184
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• 2001

현재 진행중인 정보화의 추세 속에서 도시계획 과정에서의 의사결정을 지원하기 위해 다양한 지식기반 공간분석 시스템이 개발되었으나 비구조화된 도시계획 문제의 특성으로 인해 그 적용이 어렵다. 이같은 문제의 해결을 위해 본 연구에서는 사례기반 추론에 의한 공간분석 시스템을 개발하고 이를 택지개발사업시 토지이용계획 과정에 도입하여 그 활용 가능성을 탐색해 보았다. 사례연구를 통한 실험의 결과, 개발된 공간분석 시스템의 적용 가능성이 확인되었다. 그러나 보다 충분한 양의, 건전한 내용의 사례 축적이 병행된다면 더욱 효과적인 추론을 수행할 수 있을 것이다.

• 한국정밀공학회지
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• 제23권9호
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• pp.119-126
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• 2006

This paper addresses the methodology development far the automated machining setup planning system using case-based reasoning(CBR). The case-based reasoning is used to develop a setup planning system. which consists of part input and representation module, case retrieval module, and case adaptation module. We present new approaches in the part input and representation module and the case retrieval module focusing on the similarity index determination. An illustrative example is included to demonstrate the proposed method.