• Title/Summary/Keyword: Case-based Reasoning

Search Result 445, Processing Time 0.041 seconds

Cost-Sensitive Case Based Reasoning using Genetic Algorithm: Application to Diagnose for Diabetes

  • Park Yoon-Joo;Kim Byung-Chun
    • Proceedings of the Korea Inteligent Information System Society Conference
    • /
    • 2006.06a
    • /
    • pp.327-335
    • /
    • 2006
  • Case Based Reasoning (CBR) has come to be considered as an appropriate technique for diagnosis, prognosis and prescription in medicine. However, canventional CBR has a limitation in that it cannot incorporate asymmetric misclassification cast. It assumes that the cast of type1 error and type2 error are the same, so it cannot be modified according ta the error cast of each type. This problem provides major disincentive to apply conventional CBR ta many real world cases that have different casts associated with different types of error. Medical diagnosis is an important example. In this paper we suggest the new knowledge extraction technique called Cast-Sensitive Case Based Reasoning (CSCBR) that can incorporate unequal misclassification cast. The main idea involves a dynamic adaptation of the optimal classification boundary paint and the number of neighbors that minimize the tatol misclassification cast according ta the error casts. Our technique uses a genetic algorithm (GA) for finding these two feature vectors of CSCBR. We apply this new method ta diabetes datasets and compare the results with those of the cast-sensitive methods, C5.0 and CART. The results of this paper shaw that the proposed technique outperforms other methods and overcomes the limitation of conventional CBR.

  • PDF

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

  • Lee, Hyun-Soo;Seong, Ki-Hoon;Park, Moon-Seo;Ji, Sae-Hyun;Kim, Soo-Young
    • Land and Housing Review
    • /
    • v.1 no.1
    • /
    • pp.1-7
    • /
    • 2010
  • 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.

Web Recommendation Mechanism Based on Case-Based Reasoning and Web Data Mining

  • Kim, Jin-Sung
    • Proceedings of the Korean Institute of Intelligent Systems Conference
    • /
    • 2002.12a
    • /
    • pp.443-446
    • /
    • 2002
  • In this research, we suggest a Web-based hybrid recommendation mechanism using CBR (Case-Based Reasoning) and web data mining. Data mining is used as an efficient mechanism in reasoning for relationship between goods, customers' preference and future behavior. CBR systems are normally used in problems for which it is difficult to define rules. We use CBR as an AI tool to recommend the similar purchase case. A Web-log data gathered in real-world Internet shopping mall was given to illustrate the quality of the proposed mechanism. The results showed that the CBR and web data mining-based hybrid recommendation mechanism could reflect both association knowledge and purchase information about our former customers.

Block Assembly Planning Using Case-based Reasoning and Expert System (사례기반 추론 및 전문가시스템 통합을 통한 블록조립 계획 시스템)

  • Sheen, Dong-Mok
    • Journal of Ocean Engineering and Technology
    • /
    • v.21 no.2 s.75
    • /
    • pp.81-86
    • /
    • 2007
  • This paper presents a computer aided process planning system integrating case-based reasoning and expert system for block assembly in shipbuilding. Expert rules are extracted from the case-base where cases are represented as a set of constraint-satisfaction problems. Rules for the expert system are extracted by generalizing the constraints. In generalizing the constraints, parts are generalized as variables or as part-types. The system was developed with CLIPS, an expert system shell. As more cases are collected, more rules will be extracted and the existing rules will be updated.

Case-Based Reasoning Using Self-Organization Map Neural Network (자기조직화지도 신경망을 이용한 사례기반추론)

  • Kim, Yong-Su;Yang, Bo-Suk;Kim, Dong-Jo
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
    • /
    • 2002.11b
    • /
    • pp.832-835
    • /
    • 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.

  • PDF

Electrical Fire Cause Diagnosis System based on Fuzzy Inference

  • Lee, Jong-Ho;Kim, Doo-Hyun
    • International Journal of Safety
    • /
    • v.4 no.2
    • /
    • pp.12-17
    • /
    • 2005
  • This paper aims at the development of an knowledge base for an electrical fire cause diagnosis system using the entity relation database. The relation database which provides a very simple but powerful way of representing data is widely used. The system focused on database construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. In order to store and access to the information concerned with electrical fires, the key index items which identify electrical fires uniquely are derived out. The knowledge base consists of a case base which contains information from the past fires and a rule base with rules from expertise. To implement the knowledge base, Access 2000, one of DB development tools under windows environment and Visual Basic 6.0 are used as a DB building tool. For the reasoning technique, a mixed reasoning approach of a case based inference and a rule based inference has been adopted. Knowledge-based reasoning could present the cause of a newly occurred fire to be diagnosed by searching the knowledge base for reasonable matching. The knowledge-based database has not only searching functions with multiple attributes by using the collected various information(such as fire evidence, structure, and weather of a fire scene), but also more improved diagnosis functions which can be easily wed for the electrical fire cause diagnosis system.

Development of Digestion Gas Production and Dewatering Cake Management in WWTP by Using Data Mining Technology (데이터 마이닝 기법을 활용한 하수처리장 소화가스 예측 및 탈수 케이크 관리 기법 개발)

  • Kim, Dongkwan;Kim, Hyosoo;Kim, Yejin;Kim, Minsoo;Piao, Wenhua;Kim, Changwon
    • Journal of Korean Society of Environmental Engineers
    • /
    • v.37 no.1
    • /
    • pp.1-6
    • /
    • 2015
  • The purpose of this study is to suggest the effective operation method by developing prediction model for the gas production rate, an indicator of the effectiveness of anaerobic digestion tank, using data mining. At the result, gas production estimate model is developed by using ANN within 10% error. It is expected to help operation of anaerobic digestion by suggesting selected parameter. Meanwhile case based reasoning is applied to develop dewatering cake management technology. Case based reasoning uses the most similar examples of past when a new problem occurs, therefore in this study, management measures are developed that proposes dewatering cake minimization with the minimum change by applying the case based reasoning to sludge disposal process.

공정계획 전문가시스템의 개발-조선 블럭분할에의 응용

  • 박병태;이재원
    • Proceedings of the Korean Society of Precision Engineering Conference
    • /
    • 1993.04b
    • /
    • pp.370-374
    • /
    • 1993
  • This paper describes a study on the expert system based process planning of the block division process in shipbuilding. The prototype system developed deterines the block division line of the midship of crude-oil tanker. Case-based reasoning (CBR) approach relying on previous similar cases to solve the problem is applied instead of rule-based reasoning (RBR). Similar cases are retrieved from case base according to the similarity metrics between input problem and cases. The retrieved case with the highest priority is then adapted to fit to the input problem buy adaptation rules. The adapted solution is proposed as the division line for the input problem.

Hybrid Case Based Reasoning and Neural Networks Approach for Blowing Control of Basic Oxygen Furnace (전로 취련제어를 위한 신경회로망 및 사례기반추론의 통합 접근 방법)

  • 김종한;박정준;정성원;박진우
    • Proceedings of the Korean Operations and Management Science Society Conference
    • /
    • 2003.11a
    • /
    • pp.201-204
    • /
    • 2003
  • A hybrid artificial intelligence approach based on combining case based reasoning and neural networks is presented. The approach is designed to allow for solving blowing control of BOF(basic oxygen furnace), example of which lie at the core of steelmaking process control systems application in the steel industry. According to this hybrid approach, the system, when faced with a new problem, first retrieves similar cases and neural network is used to solve the problem. Experimental Results indicate that combining case based reasoning and neural network offers an efficient approach to solving control and prediction problem

  • PDF

An Application of Case-Based Reasoning in Forecasting a Successful Implementation of Enterprise Resource Planning Systems : Focus on Small and Medium sized Enterprises Implementing ERP (성공적인 ERP 시스템 구축 예측을 위한 사례기반추론 응용 : ERP 시스템을 구현한 중소기업을 중심으로)

  • Lim Se-Hun
    • Journal of Information Technology Applications and Management
    • /
    • v.13 no.1
    • /
    • pp.77-94
    • /
    • 2006
  • Case-based Reasoning (CBR) is widely used in business and industry prediction. It is suitable to solve complex and unstructured business problems. Recently, the prediction accuracy of CBR has been enhanced by not only various machine learning algorithms such as genetic algorithms, relative weighting of Artificial Neural Network (ANN) input variable but also data mining technique such as feature selection, feature weighting, feature transformation, and instance selection As a result, CBR is even more widely used today in business area. In this study, we investigated the usefulness of the CBR method in forecasting success in implementing ERP systems. We used a CBR method based on the feature weighting technique to compare the performance of three different models : MDA (Multiple Discriminant Analysis), GECBR (GEneral CBR), FWCBR (CBR with Feature Weighting supported by Analytic Hierarchy Process). The study suggests that the FWCBR approach is a promising method for forecasting of successful ERP implementation in Small and Medium sized Enterprises.

  • PDF