• The Journal of Information Systems
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• v.15 no.2
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• pp.95-123
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• 2006

It is needed to evaluate information systems actively which has already developed to improve future performance of the organization and foster the activation of information system. The introduction or development of information system also can bring about a organizational success. To measure exactly the organizational performance of information systems, it is needed to develop a new valuation model for a specific information system from a objective pint of view, as well as to equip a standard methodology using BSC measurement. The information system valuation from a objective point of view is of importance as the basic information for the decision to obtain information system. This paper takes aim at investigating a new information system valuation model and developing a information system valuation system using case-based reasoning for predicting currency value of information system in each organization. A new information system valuation system is developed as a web-enabling base. Using this, users are able to estimate the value of specific information system on a real time efficiently.

• Journal of Information Technology Applications and Management
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• v.14 no.2
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• pp.151-168
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• 2007

The corporate credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this study, the corporate credit rating model employs artificial intelligence methods including Neural Network (NN) and Case-Based Reasoning (CBR). At first we suggest three classification models, as partitioned neural networks, all of which convert multi-group classification problems into two group classification ones: Ordinal Pairwise Partitioning (OPP) model, binary classification model and simple classification model. The experimental results show that the partitioned NN outperformed the conventional NN. In addition, we put to use CBR that is widely used recently as a problem-solving and learning tool both in academic and business areas. With an advantage of the easiness in model design compared to a NN model, the CBR model proves itself to have good classification capability through the highest hit ratio in the corporate credit rating.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.950-955
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• 2003

This paper introduces an enhanced condition monitoring and diagnosis system recently developed for rotating machinery. In the system, the data aquisition/monitoring signal processing, machine condition classifier, case-based reasoning and demonstration modules are effectively integrated with user-friendliness so that machine operators can easily monitor and diagnose the status of rotating machinery in operation. Some of the new features include the directional spectrum, case-based reasoning and neural network techniques. And the demonstrator modules for fault diagnosis of a Bear driving system and for basic understanding of the rotor dynamics are provided to help the potential users better understand the system.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.305-314
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• 2002

In order to adapt to today's rapidly changing environment and gain a competitive advantage, many companies are interested in CRM(Customer Relationship Management). Especially, the product recommendation system that can be implemented by personalizing the marketing strategy becomes the focus of CRM. In this research, we employed CBR(Case-Based Reasoning) technique that can overcome the limitation of CF(Collaborative Filtering) technique. Our system recommends the books that the customer is very likely to buy next time considering the factors such as 'Personal Features of Customer,' Similarity between Book Categories' and 'Sequence of Book Purchases'. Accuracy of predicting a book-not a particular book, but in the middle level of classification that contains about 190 categories-was about 57%.

• 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.

• The Korean Journal of Financial Studies
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• v.3 no.2
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• pp.267-279
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• 1996

The prediction of future demand is a vital task in managing business operations. To this end, traditional approaches often focused on statistical techniques such as exponential smoothing and moving average. The need for better accuracy has led to nonlinear techniques such as neural networks and case based reasoning. In addition, experimental design techniques such as orthogonal arrays may be used to assist in the formulation of an effective methodology. This paper investigates a multistrategy approach involving neural nets, case based reasoning, and orthogonal arrays. Neural nets and case based reasoning are employed both separately and in combination, while orthoarrays are used to determine the best architecture for each approach. The comparative evaluation is performed in the context of an application relating to the prediction of Treasury notes.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.399-408
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• 2001

This paper presents a simultaneous optimization approach of case-based reasoning (CBR) using a genetic algorithm(GA) for the prediction of stock price index. Prior research suggested many hybrid models of CBR and the GA for selecting a relevant feature subset or optimizing feature weights. Most studies, however, used the GA for improving only a part of architectural factors for the CBR system. However, the performance of CBR may be enhanced when these factors are simultaneously considered. In this study, the GA simultaneously optimizes multiple factors of the CBR system. Experimental results show that a GA approach to simultaneous optimization of CBR outperforms other conventional approaches for the prediction of stock price index.

• The Journal of Information Systems
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• v.14 no.1
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• pp.41-65
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• 2005

The credit ration is a significant area of financial management which is of major interest to practitioners, financial and credit analysts. The components of credit rating are identified decision models are developed to assess credit rating an the corresponding creditworthiness of firms an accurately ad possble. Although many early studies demonstrate a priori which of these techniques will be most effective to solve a specific classification problem. Recently, a number of studies have demonstrate that a hybrid model integration artificial intelligence approaches with other feature selection algorthms can be alternative methodologies for business classification problems. In this article, we propose a hybrid approach using rough set theory as an alternative methodology to select appropriate attributes for case-based reasoning. This model uses rough specific interest lies in lthe stable combining of both rough set theory to extract knowledge that can guide dffective retrevals of useful cases. Our specific interest lies in the stable combining of both rough set theory and case-based reasoning in the problem of corporate credit rating. In addition, we summarize backgrounds of applying integrated model in the field of corporate credit rating with a brief description of various credit rating methodologies.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.57-67
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• 2015

This paper proposes an Fuzzy-based Risk Reasoning Driving Strategy on VANET. Its first reasoning phase consists of a WC_risk reasoning that reasons the risk by using limited road factors such as current weather, density, accident, and construction, a DR_risk reasoning that reasons the risk by combining the driving resistance with the weight value suitable for the environment of highways and national roads, a DS_risk reasoning that judges the collision risk by using the travel direction, speed. and distance of vehicles and pedestrians, and a Total_risk reasoning that computes a final risk by using the three above-mentioned reasoning. Its second speed reduction proposal phase decides the reduction ratio according to the result of Total_risk and the reduction ratio by comparing the regulation speed of road to current vehicle's speed. Its third risk notification phase works in case current driving speed exceeds regulation speed or in case the Total_risk is higher than AV(Average Value). The Risk Notification Phase informs rear vehicles or pedestrians around of a risk according to drivers's response. If drivers use a brake according to the proposed speed reduction, the precedent vehicles transfers Risk Notification Messages to rear vehicles. If they don't use a brake, a current driving vehicle transfers a Risk Message to pedestrians. Therefore, this paper not only prevents collision accident beforehand by reasoning the risk happening to pedestrians and vehicles but also decreases the loss of various resources by reducing traffic jam.

• Korean Journal of Construction Engineering and Management
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• v.7 no.2 s.30
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• pp.138-147
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• 2006

The objective of this research is to explore a more precise forecasting method by applying Case-based Reasoning (CBR). The newly suggested method 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, 1) the choice of the numbers of referring projects and 2) the better selection among three levels ? which include a 20-work package level, a 7-major work package level, and a total sum level analysis, were investigated in detail. It is concluded that selecting similar projects at $12{\sim}19%$ 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.