• Journal of the Korean Data and Information Science Society
• /
• 제14권3호
• /
• pp.641-651
• /
• 2003

This study suggests the hybrid models for interest rate forecasting using structural changes (or change points). The basic concept of this proposed model is to obtain significant intervals caused by change points, to identify them as the change-point groups, and to reflect them in interest rate forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in the U. S. Treasury bill rate dataset. The second phase is to forecast the change-point groups with data mining classifiers. The final phase is to forecast interest rates with backpropagation neural networks (BPN). Based on this structure, we propose three hybrid models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported model, (2) case-based reasoning (CBR)-supported model, and (3) BPN-supported model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the prediction ability of hybrid models to reflect the structural change.

• 지능정보연구
• /
• 제10권2호
• /
• pp.91-109
• /
• 2004

최근 채권의 상환 및 이자의 확실성 정도를 측정하고 연관된 상대적인 위험의 정도를 나타내는 채권등급 평가의 중요성이 대두되고 있다. 초기의 대다수 선행 연구들에서는 기업의 채권 등급예측을 위하여 통계적 기법이 많이 사용되었으나, 많은 연구들에 의해 그 우수성이 보고되고 있는 사례기반 추론 등 인공지능 기법들이 통계모형의 대안으로 제시되어지고 있다. 사례기반 추론에서는 과거의 사례들이 지식으로 표현되고 해결 방법으로 사용된다. 유용한 사례기반 시스템을 구축하기 위해서 시스템의 지식베이스를 구축할 사례들을 인간의 정보처리 과정과 유사한 방법으로 표현하는 것이 중요하다. 본 논문은 실제 세계의 애매모호한 사례들을 다루는데 적절한 퍼지집합개념을 사례기반 추론과 결합하는 통합 방법론을 제시하고자 한다. 퍼지집합이론은 인간이 의사결정시 사용하는 유사한 자연스러운 언어를 수학적으로 변환할 수 있게 해주는 인공지능 기법이다.

• 한국건설관리학회:학술대회논문집
• /
• 한국건설관리학회 2004년도 제5회 정기학술발표대회 논문집
• /
• pp.117-124
• /
• 2004

Dynamic and fragmented characteristics ale two of the most significant factors that distinguish the construction industry from other industries. Previous forecasting techniques have failed to solve the problems derived from the above characteristics and do not provide considerable support. This paper deals with providing a more precise forecasting by applying Case-based Reasoning (CBR). The newly developed model in this study enables project managers to forecast monthly expenditures with less time and effort by retrieving and referring only projects of a similar nature, while filtering out irrelevant cases included in database. For the purpose of accurate forecasting. the choice of the numbers of referring projects was investigated. it is concluded that selecting similar projects at $5\~6\;\%$ out of the whole database will produce a more precise forecasting. The new forecasting model. which suggests the predicted values based on previous projects, is more than just a forecasting methodology it provides a bridge that enables current data collection techniques to be used within the context of the accumulated information. This will eventually help all the participants in the construction industry to build up the know ledge derived from invaluable experience.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2006년도 추계학술대회 학술발표 논문집 제16권 제2호
• /
• pp.39-44
• /
• 2006

Ship's collision avoidance is a skill that masters of merchant marine vessels have acquired through years of experience and that makes them feel at ease to guide their ship out from danger quickly compared to inexperienced officers. Case based reasoning(CBR) uses the same technique in solving tasks that needs reference from variety of situations. CBR can render decision-making easier by retrieving past solutions from situations that are similar to the one at hand and make necessary adjustments in order to adapt them. In this paper, we propose to utilize the advantages of CBR in a support system for ship's collision avoidance while using fuzzy algorithm for its retrieval of similar navigational situations, stored in the casebase, thus avoiding the cumbersome tasks of creating a new solution each time a new situation is encountered. There will be two levels within the Fuzzy-CBR. The first level will identify the dangerous ships and index the new case. The second level will retrieve cases from casebase and adapt the solution to solve for the output. While CBR's accuracy depends on the efficient retrieval of possible solutions to be adapted from stored cases, fuzzy algorithm will improve the effectiveness of solving the similarity to a new case at hand.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
• /
• pp.340-343
• /
• 1996

실세계에서 존재하는 대부분의 지식은 다양한 패턴들로 구성되어 있다. 본 논문에서는 사례베이스 추론(Case-Based Reasoning : CBR)에서 다중의 의미를 갖는 불확실한 지식을 쉽게 표현할 수 있는 러프 집합을 이용하여 지식의 함축의 의미를 갖는 지식을 간략화하는 방법을 제안한다. 전문가의 지식 구조를 명확화 하는데는 많은 노력이 필요하고 지식획득의 병목현상이 일어난다. 이러한 문제점을 해결하기 위해 많은 사례의 수를 러프 집합의 성질을 이용하여 사례를 동치 클래스로 분류하여 사례의 수를 감소하므로써 CBR의 기능을 향상시킨다.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2002년도 추계정기학술대회
• /
• pp.491-501
• /
• 2002

Case-based reasoning is emerging as a leading methodology for the application of artificial intelligence. CBR is a reasoning methodology that exploits similar experienced solutions, in the form of past cases, to solve new problems. Hybrid model achieves some convergence of the wide proliferation of credit evaluation modeling. As a result, Hybrid model showed that proposed methodology classify more accurately than any of techniques individually do. It is confirmed that proposed methodology predicts significantly better than individual techniques and the other combining methodologies. The objective of the proposed approach is to determines a set of weighting values that can best formalize the match between the input case and the previously stored cases and integrates fuzzy sit concepts into the case indexing and retrieval process. The GA is used to search for the best set of weighting values that are able to promote the association consistency among the cases. The fitness value in this study is defined as the number of old cases whose solutions match the input cases solution. In order to obtain the fitness value, many procedures have to be executed beforehand. Also this study tries to transform financial values into category ones using fuzzy logic approach fur performance of credit evaluation. Fuzzy set theory allows numerical features to be converted into fuzzy terms to simplify the matching process, and allows greater flexibility in the retrieval of candidate cases. Our proposed model is to apply an intelligent system for bankruptcy prediction.

• 한국멀티미디어학회논문지
• /
• 제9권9호
• /
• pp.1095-1106
• /
• 2006

전자상거래시스템의 효율적인 운영과 관리를 위해서 더욱 많은 노력이 요구되고 있으며 고객의 요구에 대해서 가장 적절한 상품 정보를 제공함으로서 만족을 극대화할 수 있어야 한다. 이를 위해서 많은 지능형 에이전트기술을 사용한 전자상거래시스템이 도입되고 있다. 본 논문에서는 전자상거래시스템에서 개인 상품 추천 지원을 위한 사례기반추론기법과 다중크로스 셀링기법(Multi-Crossselling)을 기반으로 한 상품 추천시스템을 제안하였다. 제안한 시스템은 다중크로스셀링 기법을 통해 고객패턴의 유사값에 가까운 여러 상품을 추출하고 사례기반추론기법을 통해 특정 조건에서 고객의 요구에 대해 적절한 상품 정보를 제공하고자 한다.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2002년도 춘계학술대회 및 임시총회
• /
• pp.81-86
• /
• 2002

In current CBR(Case-Based Reasoning) systems, the case adaptation is usually performed by rule-based method that use rules hand-coded by the system developer. So, CBR system designer faces knowledge acquisition bottleneck similar to those found in traditional expert system design. In this thesis, 1 present a model for learning method of case adaptation knowledge using case base. The feature difference of each pair of cases are noted and become the antecedent part of an adaptation rule, the differences between the solutions in the compared cases become the consequent part of the rule. However, the number of rules that can possibly be discovered using a learning algorithm is enormous. The first method for finding cases to compare uses a syntactic measure of the distance between cases. The threshold fur identification of candidates for comparison is fixed th the maximum number of differences between the target and retrived case from all retrievals. The second method is to use similarity metric since the threshold method may not be an accurate measure. I suggest the elimination method of duplicate rules. In the elimination process, a confidence value is assigned to each rule based on its frequency. The learned adaptation rules is applied in riven target Problem. The basic. process involves search for all rules that handle at least one difference followed by a combination process in which complete solutions are built.

• 지능정보연구
• /
• 제11권1호
• /
• pp.107-121
• /
• 2005

최근, 추천시스템과 협업 필터링에 대한 연구가 학계와 업계에서 활발하게 이루어지고 있다. 하지만, 제품 아이템들은 다중 값 속성을 가질 수 있음에도 불구하고, 기존의 연구들은 이러한 다중 값 속성을 반영하지 못하고 있다. 이러한 한계를 극복하기 위하여, 본 연구에서는 추천시스템을 위한 새로운 방법론을 제시하고자 한다. 제안된 방법론은 제품 아이템에 대한 클러스터링 기법에 기반하여 다중 값 속성을 팔용하며, 정확한 추천을 위하여 협업 필터링을 적용한다. 즉, 사용자간의 상관관계만이 아니라 아이템간의 상관관계를 고려하기 위하여, 사용자 클러스터링에 기반한 사례기반추론과 아이템 속성 클러스터링에 기반한 사례기반추론 모두가 협업 필터링에 적용되는 것이다. 다중 값 속성에 기반하여 아이템을 클러스터링 함으로써, 아이템의 특징이 명확하게 식별될 수 있다. MovieLens 데이터를 이용하여 실험을 하였으며, 제안된 방법론이 기존 방법론의 성능을 능가한다는 결과를 얻을 수 있었다.

• Architectural research
• /
• 제8권1호
• /
• pp.77-84
• /
• 2006

In spite of enormous increase in data generation, its practical usage in the construction sector has not been prevalent enough compared to those of other industries. The author would explore the obstacles against efficient data application in the arena of expenditure forecasting, and suggest a forecasting method by applying Case-based Reasoning (CBR). The newly suggested method in the research, enables project managers to forecast monthly expenditures with less time and effort by retrieving and referring only projects of a similar nature, while filtering out irrelevant cases included in database. Among 99 projects collected, the cost data from 88 projects were processed to establish a new forecasting model. The remaining 10 projects were utilized for the validation of the model. From the comprehensive study, the choice of the numbers of referring projects was investigated in detail. It is concluded that selecting similar projects at 12~19 % out of the whole database will produce a more precise forecasting. The new forecasting model, which suggests the predicted values based on previous projects, is more than just a forecasting methodology; it provides a bridge that enables current data collection techniques to be used within the context of the accumulated information. This will eventually help all the participants in the construction industry to build up the knowledge derived from invaluable experience.