• Journal of Intelligence and Information Systems
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• v.3 no.1
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• pp.31-45
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• 1997

To date, research on data mining has focused primarily on individual techniques to su, pp.rt knowledge discovery. However, the integration of elementary learning techniques offers a promising strategy for challenging a, pp.ications such as forecasting nonlinear processes. This paper explores the utility of an integrated a, pp.oach which utilizes a second-order learning process. The a, pp.oach is compared against individual techniques relating to a neural network, case based reasoning, and induction. In the interest of concreteness, the concepts are presented through a case study involving the prediction of network traffic for video-on-demand.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.383-386
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• 2005

This study proposes a case based reasoning system with two dimensional reduction techniques. In this study, vertical and horizontal dimensions of the research data are reduced through hybrid feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed technique may improve the classification accuracy and outperform various optimized models of typical CBR system.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.187-194
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• 1999

Case-Based Reasoning(CBR) systems support ill structured decision-making. The measure of the success of a CBR system depends on its ability to retrieve the most relevant previous cases in support of the solution of a new case. One of the methodologies widely used in existing CBR systems to retrieve previous cases is that of the Nearest Neighbor(NN) matching function. The NN matching function is based on assumptions of the independence of attributes in previous case and the availability of rules and procedures for matching.(omitted)

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1496-1515
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• 2017

One of the most visible developments in Decision Support Systems (DSS) was the emergence of rule-based expert systems. Hence, despite their success in many sectors, developers of Medical Rule-Based Systems have met several critical problems. Firstly, the rules are related to a clearly stated subject. Secondly, a rule-based system can only learn by updating of its rule-base, since it requires explicit knowledge of the used domain. Solutions to these problems have been sought through improved techniques and tools, improved development paradigms, knowledge modeling languages and ontology, as well as advanced reasoning techniques such as case-based reasoning (CBR) which is well suited to provide decision support in the healthcare setting. However, using CBR reveals some drawbacks, mainly in its interrelated tasks: the retrieval and the adaptation. For the retrieval task, a major drawback raises when several similar cases are found and consequently several solutions. Hence, a choice for the best solution must be done. To overcome these limitations, numerous useful works related to the retrieval task were conducted with simple and convenient procedures or by combining CBR with other techniques. Through this paper, we provide a combining approach using the multi-criteria analysis (MCA) to help, the traditional retrieval task of CBR, in choosing the best solution. Afterwards, we integrate this approach in a decision model to support medical decision. We present, also, some preliminary results and suggestions to extend our approach.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.201-202
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• 1999

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.3
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• pp.81-101
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• 2003

The financial statements purpose to provide useful information to decision-making process of business managers. The value-relevant information, however, embedded in the financial statement has been often overlooked in Korea. In fact, the financial statements in Korea have been utilized for nothing but account reports to Security Supervision Boards (SSB). The objective of this study is to develop earnings forecasting models through financial statement analysis using artificial intelligence (AI). AI methods are employed in forecasting earnings: artificial neural networks (ANN) for manufacturing industry and case~based reasoning (CBR) for banking industry. The experimental results using such AI methods are as follows. Using ANN for manufacturing industry records 63.2% of hit ratio for out-of-sample, which outperforms the logistic regression by around 4%. The experiment through CBR for banking industry shows 65.0% of hit ratio that beats the statistical method by 13.2% in holdout sample. Finally, the prediction results for manufacturing industry are validated through monitoring the shift in cumulative returns of portfolios based on the earning prediction. The portfolio with the firms whose earnings are predicted to increase is designated as best portfolio and the portfolio with the earnings-decreasing firms as worst portfolio. The difference between two portfolios is about 3% of cumulative abnormal return on average. Consequently, this result showed that the financial statements in Korea contain the value-relevant information that is not reflected in stock prices.

• The Journal of the Korea Contents Association
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• v.1 no.1
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• pp.51-58
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• 2001

Electronic commerce system must provide convenient interface, easy and fast searching function, and production information satisfying customers. To do this, many kinds of studies are being advanced actively about electronic commerce system using intelligent agent electronic This paper suggests 2-tier electronic commerce system using intelligent multi agent. We propose a combined reasoning agent system which provides production information satisfying customer's needs using both case-based reasoning and rule-based reasoning. And this system distribute network and sewer system load based on load balancing and 2-tier agent structure. This system can find production information through teaming of rule-based reasoning method and case-based reasoning method. This system can provide the best suitable production information to customers by using combined reasoning agent system. And we can prevent customer's unexpected long waiting causes by network traffic and server load.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.1
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• pp.109-141
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• 1998

Many processes in the industrial realm exhibit sstochastic and nonlinear behavior. Consequently, an intelligent system must be able to nonlinear production processes as well as probabilistic phenomena. In order for a knowledge based system to control a manufacturing processes as well as probabilistic phenomena. In order for a knowledge based system to control manufacturing process, an important capability is that of prediction : forecasting the future trajectory of a process as well as the consequences of the control action. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes, includinb chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a chaotic process in the presence of various patterns of noise.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05a
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• pp.341-344
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• 2003

사례 기반 추론(case-based reasoning)은 현재의 문제를 해결하기 위해서 과거에 유사하게 수행된적이 있는 사례를 유추하여, 유추된 사례의 해를 이용하는 기법으로서 규칙 기반 추론과 함께 여러분야에 응용되고 있다. 하지만 사례기반 추론 시 새로운 사례를 해결하기 위하여 사례베이스 안의 모든 사례를 검색해야 하기 때문에 수행시간이 증가되는 문제점을 지니고 있다. 본 연구에서는 규칙 및 K-Means 클러스터링 알고리즘에 의한 사례 기반 추론을 이용한 ADS-DAAP(Advanced Diagnosis System for Diseases associated with Acute Abdominal Pain)를 제안한다. 제안하는 시스템은 기존의 CDS-DAAP(Combined Diagnosis System for Diseases associated with Acute Abdominal Pain)와 비교해 볼 때, 수행시간을 감소시켰다.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.263-270
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• 2002

Most conventional systems of fire fighting control in a ship have been based on rule-based system in which expert knowledges are expressed with production rules. Renewing and adding of rules is needed continuously for the improvement of the system capability in an already build-up system and such adding and renewing procedures could hinder users from fluent utilization of a system. The author proposes an advanced fire fighting control intelligent system (A-FFIS) using rule-based and carte-based reasoning by clustering to implement conventional hybrid system (H-FFIS). Compared with H-FFIS, new approach with A-FFIS shows that the system proposed here improves fire detection rate and reduces fire detection time.