• Proceedings of the Korean Institute of Building Construction Conference
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• 2006.05a
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• pp.119-124
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• 2006

Design-Build, one contractor is responsible for both the design and construction, has become more popular since the government framed the policy on how to activate the design-build projects in 1996. The reality is, however, there are many problems encounted on Contractor Selection Method for Public Design-Build Projects. The purpose of this paper is to improve the problems, no way to meet the goal(value) the owners expect from the design-build projects, for instance, not fully reflecting the characteristics of projects and owners intention, not systematical enough to judge if bidders could carry out the contract. This study will insist we introduce Best Value Procurement, which is being commonly used in some advanced countries recently, so that we would properly select the contractor suitable for Best Value concept which totally depends on the owners, types of work and specified conditions. Furthermore, by passing through the Two-Step Procedures following Pre-qualification in Best Value Procurement, we expect it lighten the bidders' burden for proposal and the owners' complicate bid administration.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.516-525
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• 2005

Case-based reasoning (CBR) has been widely used in various areas due to its convenience and strength in complex problem solving. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most prior studies have tried to optimize the weights of the features or selection process of appropriate instances. But, these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than in naive models. In particular, there have been few attempts to simultaneously optimize the weight of the features and selection of the instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm (GA). We apply it to a customer classification model which utilizes demographic characteristics of customers as inputs to predict their buying behavior for a specific product. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.920-926
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• 2005

Nearest neighbor algorithms classify an unseen input instance by selecting similar cases and use the discovered membership to make predictions about the unknown features of the input instance. The usefulness of the nearest neighbor algorithms have been demonstrated sufficiently in many real-world domains. In nearest neighbor algorithms, it is an important issue to assign proper weights to the attributes. Therefore, in this paper, we propose a new method which can automatically assigns to each attribute a weight of its importance with respect to the target attribute. The method has been implemented as a computer program and its effectiveness has been tested on a number of machine learning databases publicly available.

• The KIPS Transactions:PartB
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• v.16B no.3
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• pp.233-238
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• 2009

This paper presents an approach to the classifications of small size document using the instance-based feature filtering Relief-F algorithm. In the document classifications, we have not always good classification performances of small size document included a few features. Because total number of feature in the document set is large, but feature count of each document is very small relatively, so the similarities between documents are very low when we use general assessment of similarity and classifiers. Specially, in the cases of the classification of web document in the directory service and the classification of the sectors that cannot connect with the original file after recovery hard-disk, we have not good classification performances. Thus, we propose the Extended Relief-F(ERelief-F) algorithm using instance-based feature filtering algorithm Relief-F to solve problems of Relief-F as preprocess of classification. For the performance comparison, we tested information gain, odds ratio and Relief-F for feature filtering and getting those feature values, and used kNN and SVM classifiers. In the experimental results, the Extended Relief-F(ERelief-F) algorithm, compared with the others, performed best for all of the datasets and reduced many irrelevant features from document sets.

• Korean journal of food and cookery science
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• v.29 no.5
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• pp.553-562
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• 2013

The purpose of this study was to analyze the key factors considered important by customers in the selection of a Korean restaurant through the use of conjoint analysis techniques. A total of 400 questionnaires were distributed over a 2-week period in October 2011, of which 354 were completed (88.5%). Statistical analysis was then carried out using the Windows 18.0 Statistics package. The research was based on the analysis of two target areas - daily meals and special purpose meals. Responses were measured by using Zaichkowsky's Personal Involvement Inventory (PII) and a 7-point Likert Scale. Overall it was found that in all areas of the results regarding the involvement related analyses, daily meals scored lower than special purpose meals. This implied that the choice of daily meals is more applicable to customers with a low level of involvement, whereas high-involvement customers were more likely to focus on special purpose meals. The analysis of high-involvement customers revealed that the quality of food, price, service quality and physical environment, in order of priority, were the most important factors in selecting a restaurant. The use of the optimum attribute combination revealed the following results: delicious food (0.601); friendly staff (0.170); clean restaurant (0.191); price of 20,000 won (-0.513). Furthermore, low-involvement customers considered the following factors as important when selecting a Korean restaurant: quality of food, followed by price, physical environment and service quality in that order. In this instance, the optimum attribute combination showed the following outcomes: tasty food (0.645); friendly staff (0.418); clean restaurant (0.365); price of 5,000 won (-0.847). These results indicated the importance of developing a marketing plan which was based specifically on a customer's involvement level, focusing on their main selection criteria when choosing a Korean restaurant.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.3
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• pp.109-124
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• 2000

For rational human value judgement and evaluation, provision of clear evaluation data, objective value judgement criteria, and properly generalized methods are required. For instance, this is true for software quality evaluation, and the measure of software quality and the weighting method of evaluation target directly affect final decisions. However it is not easy to find a generalized method for the software quality evaluation or product selection, because of its complex characteristics. In this paper, we apply the qualitative preference method based on quantitative belief functions to find a general weighing method for the software quality evaluation. In particular, the qualitative preference method, in which the differentiated preference expression is possible, is conceptually expanded for general applications in future. For this purpose, we hierarchically differentiate the strong preference relation from the weak preference relation, and show an example of quantification of software quality evaluation on different applications, by comparing the qualitative preference method with AHP. We believe that the application domain of this method is not limited to the software quality evaluation and it is very useful to apply this results to other SE areas, e.g., metric selection with different views and riority determination of practices to be assessed in the SPICE.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.5
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• pp.437-444
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• 2015

In simulation-based circuit optimization, many simulation runs may be wasted while evaluating infeasible designs, i.e. the designs that do not meet the constraints. To avoid such a waste, this paper investigates the use of support vector machine (SVM) classifiers in predicting the design's feasibility prior to simulation and the optimal selection of the SVM parameters, namely, the Gaussian kernel shape parameter ${\gamma}$ and the misclassification penalty parameter C. These parameters affect the complexity as well as the accuracy of the model that SVM represents. For instance, the higher ${\gamma}$ is good for detailed modeling and the higher C is good for rejecting noise in the training set. However, our empirical study shows that a low ${\gamma}$ value is preferable due to the high spatial correlation among the circuit design candidates while C has negligible impacts due to the smooth and clean constraint boundaries of most circuit designs. The experimental results with an LC-tank oscillator example show that an optimal selection of these parameters can improve the prediction accuracy from 80 to 98% and model complexity by $10{\times}$.

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.209-213
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• 2002

This paper describes the reduction of DB without degradation of speech quality in Corpus-based Speech synthesizer of Korean language. In this paper, it is proposed that the frequency of every unit in reduced DB should reflect the frequency of units in Korean language. So, the target population of every unit is set to be proportional to their frequency in Korean large corpus(780K sentences, 45Mega phonemes). Second, the frequent instances during synthesis should be also maintained in reduced DB. To the last, it is proposed that frequency of every instance should be reflected in clustering criterion and used as criterion for selection of representative instances. The evaluation result with proposed methods reveals better quality than using conventional methods.

• Korean Journal of Human Ecology
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• v.9 no.1
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• pp.63-70
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• 2000

The purpose of this study is The results was that consumers who like to the top brand's commodities, had commonly high tendency to and fro its trademark and store allegiance. Furthermore, they have usually bought something following on their inclination what they had purchased as well as the store decoration character and the marketing promotion attribute. The other consumers who prefer to the discount store's merchandises, had also high propensity and the biggest influence on buying something which were those factors; their instance shopping habit, utility-economy trait, follow the fashion character and strong circumspection tendency besides using the mass media Info., personal data and commodities' attribute.

• MALSORI
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• no.44
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• pp.145-156
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• 2002

This paper describes the reduction of DB without degradation of speech quality in Corpus-based Speech synthesizer of the Korean language. In this paper, it is proposed that the frequency of every unit in reduced DB reflect the frequency of units in the Korean language. So, the target population of every unit is set to be proportional to its frequency in Korean large corpus (780k sentences, 45Mega phones). Secondly, the frequent instances during synthesis should be also maintained in reduced DB. To the last, it is proposed that frequency of every instance be reflected in clustering criteria and used as another important criterion for selection of representative instances. The evaluation result with proposed methods reveals better quality than that using conventional methods.