• Journal of Korea Technology Innovation Society
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• v.10 no.2
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• pp.183-205
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• 2007

An investigation was undertaken of the optimal discriminant model for predicting the likelihood of insolvency in advance for medium-sized firms based on the technology evaluation. The explanatory variables included in the discriminant model were selected by both factor analysis and discriminant analysis using stepwise selection method. Five explanatory variables were selected in factor analysis in terms of explanatory ratio and communality. Six explanatory variables were selected in stepwise discriminant analysis. The effectiveness of linear discriminant model and logistic discriminant model were assessed by the criteria of the critical probability and correct classification rate. Result showed that both model had similar correct classification rate and the linear discriminant model was preferred to the logistic discriminant model in terms of criteria of the critical probability In case of the linear discriminant model with critical probability of 0.5, the total-group correct classification rate was 70.4% and correct classification rates of insolvent and solvent groups were 73.4% and 69.5% respectively. Correct classification rate is an estimate of the probability that the estimated discriminant function will correctly classify the present sample. However, the actual correct classification rate is an estimate of the probability that the estimated discriminant function will correctly classify a future observation. Unfortunately, the correct classification rate underestimates the actual correct classification rate because the data set used to estimate the discriminant function is also used to evaluate them. The cross-validation method were used to estimate the bias of the correct classification rate. According to the results the estimated bias were 2.9% and the predicted actual correct classification rate was 67.5%. And a threshold value is set to establish an in-doubt category. Results of linear discriminant model can be applied for the technology financing banks to evaluate the possibility of insolvency and give the ranking of the firms applied.

• Journal of Korea Technology Innovation Society
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• v.9 no.2
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• pp.304-324
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• 2006

There were considerable researches by finance people trying to find out business ratios as predictors of corporate bankruptcy. However, such financial ratios usually lack theoretical justification to predict bankruptcy for technology-oriented small sized venture firms. This study proposes a bankruptcy predictive discriminant model using technology evaluation data instead of financial data, evaluates the model fit by the correct classification rate, cross-validation method and M-P-P method. The results indicate that linear discriminant model was found to be more appropriate model than the logistic discriminant model and 69% of original grouped data were correctly classified while 67% of future data were expected to be classified correctly.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.298-299
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• 2018

In this study, the discriminant power of the DEA models which do not require the prior information of decision makers was evaluated. Entropy model, Bootstrap model, Benevolent Cross Efficiency model, Aggressive Cross Efficiency model and Game Cross Efficiency model were selected as the DEA model for discriminant power evaluation. The discriminant power of five DEA models was evaluated using coefficient of variation and degree of importance. According to the evaluation results, the rank of discrimination power was evaluated in the order of Entropy model, Aggressive CE model, Benevolent CE model, Game CE model, and Bootstrap model in both evaluation indexes.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.455-468
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• 2008

Based on the huge baseball game records, the steal plays an important role to affect the result of games. For the research about success or failure of the steal in baseball games, logistic regression models are developed based on 2007 Korean professional baseball games. The analyses of logistic regression models are compared of those of the discriminant models. It is found that the performance of the logistic regression analysis is more efficient than that of the discriminant analysis. Also, we consider an alternative logistic regression model based on categorical data which are transformed from uneasy obtainable continuous data.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.1
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• pp.16-21
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• 2001

전자 상거래에서 고객의 성향을 이해하기 위해서는 일반적으로 판매 실무에서의 경험과 전문적인 지식을 필요로 하게 된다. 데이터 마이닝은 고객들에 대한 데이터의 분석을 통해서 이러한 성향들을 알아내는 것을 목표로 한다. 베이지안 네트워크는 DAG(Directed Acyclic Graph)를 이용하여 데이터의 구조를 시각적으로 표현하여 주는 확률모형으로 변수사이의 종속관계를 밝히고 데이터 마이닝의 기법으로 이용할 수 있다. 본 논문에서는 베이지안 네트워크를 사용하여 전자 상거래 고객들의 성향을 분석하기 위한 방법을 제시한다. 또한 고객성향에 대한 주요 요인을 분석하기 위해 Discriminant 모형을 이용하고 그 유용성을 다른 방법들과 비교하였다.

• Journal of Korean Society of Transportation
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• v.28 no.6
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• pp.147-158
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• 2010

Studies on the distribution of traffic demands have been proceeding by providing traffic information for reducing greenhouse gases and reinforcing the road's competitiveness in the transport section, however, since it is preferentially required the extensive studies on the driver's behavior changing routes and its influence factors, this study has been developed a discriminant model for changing routes considering driving conditions including traffic conditions of roads and driver's preferences for information media. It is divided into three groups depending on driving conditions in group classification with the CART analysis, which is statistically meaningful. And, elements of the driving conditions and the preferred media affecting the change of paths are classified into statistical meaningful groups through the CHAID analysis, and the major factors affecting the change of paths are examined. Finally, the extent that driving conditions and preferred media affect a route change is examined through a discriminant analysis, and it is developed a discriminant model equation to predict a route change. As a result of building the discriminant model equation, it is shown that driving conditions affect a route change much more, the entire discriminant hit ratio is derived as 64.2%, and this discriminant equation shows high discriminant ability more than a certain degree.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.1-13
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• 2002

In this paper, we compares the business failure prediction accuracy among Linear Programming Discriminant Analysis(LPDA) model, Multivariate Discriminant Analysis (MDA) model and logit analysis model. The Data for 417 companies analyzed were gathered from KIS-FAS Published by Korea Information Service in 1999. The result of comparison for four time horizons shows that LPDA Is advantageous in prediction accuracy over the other two models when over all tilt ratio and business failure accuracy are considered simultaneously.

• Journal of Korean Society for Quality Management
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• v.23 no.2
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• pp.53-67
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• 1995

The problem considered here is to find the optimal discriminant analysis method in growth curve model. It has been studied how to find correct prior probability for the effective classification in discriminant analysis. We use the balanced condition to calculate prior probability. From the informative simulation study, new classification rule for the growth curve model is suggested. The suggested classification rule has better classification result than the other previously suggested method in terms of error rate criterion.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.1
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• pp.1-15
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• 2016

This study aims to develop a model by which city bus drivers who are likely to cause an accident can be figured out based on the information about their actual driving records. For this purpose, from the information about the actual driving records of the drivers who have caused an accident and those who have not caused any, significance variables related to traffic accidents are drawn, and the accuracy between models is compared for the classification models developed, applying a discriminant analysis and logistic regression analysis. In addition, the developed models are applied to the data on other drivers' driving records to verify the accuracy of the models. As a result of developing a model for the classification of drivers who are likely to cause an accident, when deceleration ($X_{deceleration}$) and acceleration to the right ($Y_{right}$) are simultaneously in action, this variable was drawn as the optimal factor variable of the classification of drivers who had caused an accident, and the prediction model by discriminant analysis classified drivers who had caused an accident at a rate up to 62.8%, and the prediction model by logistic regression analysis could classify those who had caused an accident at a rate up to 76.7%. In addition, as a result of the verification of model predictive power of the models showed an accuracy rate of 84.1%.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.3
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• pp.467-473
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• 2011

The contractor management for the effective defense project is essential factor in the modern defense acquisition task. The occurrence of unlawful company causes hastiness for project manager and setback to the deployment of defense weapon system. In this paper, we develop a prediction model for the effective defense project by using the discriminant analysis and analyse the variables that discriminate the unlawful company in many variables. It is expected that our model can be used to improve the project management capability of defense acquisition and contribute to the establishment of efficient procurement procedure through entry of the reliable defense manufacturer.