• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.73-84
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• 2005

Highway design criteria are considering roadway safety and smooth driving maneuver. However, a certain highway alignment within design criteria often leads drivers to undesirable situation due to the differences between the original intention of design criteria and the unintended result of drivers' cognition. The differences between them often result in traffic accidents. In order to reduce accident process, highway safety facilities are installed on those roadway sections. However, the relationship between highway environments and human factors has not been deeply studied in Korea. In this study. vertical roadway sections are constructed with 3-D graphical tools. This vertical roadway sections are simulated on a driving simulator in order to identify the differences of drivers' cognition on different roadway environments. Based upon the collected data from the driving simulator, canonical correlation analysis and canonical discriminant analysis of quantification theory II have been performed in order to figure out impacting factors on the degree of roadway safety. Also, based upon quantification theory I. the relationship between roadway safety facilities and the degree of safety has been analyzed.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.473-483
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• 2014

Even though the ROC manifold for more than three dimensional space which is an extension of the ROC curve and surface has difficulty to represent graphically, the hypervolume under the ROC manifold (HUM) statistic can be defined and obtained based on AUC and VUS measures for the ROC curve and the ROC surface. Hence the definition and characteristics of the HUM for four dimensional space are studied in this work. By extension of the standard criterion of AUC for probabilities of default based on Basel II, the 13 classes of standard criterion of HUM are proposed in order to discriminate four classification models and some application methods are discussed. In order to explore the standard criterion of HUM whose values are obtained from various distributions, ternary plot is used and explained.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.82-89
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• 2019

The reason why NCS education is conducted in college is because students are expected to improve their skills through practical education. In addition, NCS education is expected to have a positive impact on job competence and competitiveness. The purpose of this study is to explain the necessity of NCS education, and to analyze whether the education transition and practical performance are manifested. For this purpose, the research model was composed of "NCS Education ${\Rightarrow}$ Educational Transition ${\Rightarrow}$ Practical Results" and surveyed about 300 college students in the metropolitan area. If NCS education is necessary in practice, colleges and other educational institutions should plan to make NCS education operate efficiently. As a analysis result, it was confirmed that the internal consistency was secured and both the intensive validity & discriminant validity were confirmed in the validity. As a result of using the structural equation model, this research model was found to be suitable. As a result of the hypothesis test, it is analyzed that the standardization coefficient of the research hypothesis is above the appropriate level and the correlation is highly formed.

• Journal of Korean Academy of Nursing
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• v.45 no.2
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• pp.159-168
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• 2015

Purpose: The purpose of this study was to evaluate the methodological quality of nursing studies using structural equation modeling in Korea. Methods: Databases of KISS, DBPIA, and National Assembly Library up to March 2014 were searched using the MeSH terms 'nursing', 'structure', 'model'. A total of 152 studies were screened. After removal of duplicates and non-relevant titles, 61 papers were read in full. Results: Of the sixty-one articles retrieved, 14 studies were published between 1992 and 2000, 27, between 2001 and 2010, and 20, between 2011 and March 2014. The methodological quality of the review examined varied considerably. Conclusion: The findings of this study suggest that more rigorous research is necessary to address theoretical identification, two indicator rule, distribution of sample, treatment of missing values, mediator effect, discriminant validity, convergent validity, post hoc model modification, equivalent models issues, and alternative models issues should be undergone. Further research with robust consistent methodological study designs from model identification to model respecification is needed to improve the validity of the research.

• The Journal of Information Systems
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• v.27 no.3
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• pp.63-80
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• 2018

Purpose The purpose of this study is to build a machine learning-based customer classification model to promote customer expansion effect of the free sample promotion. Specifically, the proposed model classifies potential target customers who are expected to purchase the products included in the free sample promotion after receiving the free samples. Design/methodology/approach This study proposes to build a customer classification model for determining customers suitable for providing free samples by using various machine learning techniques such as logistic regression, multiple discriminant analysis, case-based reasoning, decision tree, artificial neural network, and support vector machine. To validate the usefulness of the proposed model, we apply it to a real-world free sample-based target marketing case of a Korean major cosmetic retail company. Findings Experimental results show that a machine learning-based customer classification model presents satisfactory accuracy ranging from 70% to 75%. In particular, support vector machine is found to be the most effective machine learning technique for free sample-based target marketing model. Our study sheds a light on customer relationship management strategies using free sample promotions.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.541-553
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• 2020

The receiver operating characteristic (ROC) curve is expressed as both sensitivity and specificity; in addition, some optimal thresholds using the ROC curve are also represented with both sensitivity and specificity. In addition to the sensitivity and specificity, the expected usefulness function is considered as disease prevalence and usefulness. In particular, partial the area under the ROC curve (AUC) on a certain range should be compared when the AUCs of the crossing ROC curves have similar values. In this study, partial AUCs representing high sensitivity and specificity are proposed by using sensitivity and specificity lines, respectively. Assume various distribution functions with ROC curves that are crossing and AUCs that have the same value. We propose a method to improve the discriminant power of the classification models while comparing the partial AUCs obtained using sensitivity and specificity lines.

• Health Policy and Management
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• v.12 no.2
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• pp.1-22
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• 2002

Since the early 1980s scholars have applied latent structure and other type of finite mixture models from various academic fields. Although the merits of finite mixture model are well documented, the attempt to apply the mixture model to medical service has been relatively rare. The researchers aim to try to fill this gap by introducing finite mixture model and segmenting inpatients DB from one general hospital. In section 2 finite mixture models are compared with clustering, chi-square analysis, and discriminant analysis based on Wedel and Kamakura(2000)'s segmentation methodology schemata. The mixture model shows the optimal segments number and fuzzy classification for each observation by EM(expectation-maximization algorism). The finite mixture model is to unfix the sample, to Identify the groups, and to estimate the parameters of the density function underlying the observed data within each group. In section 3 and 4 we illustrate results of segmenting 4510 patients data including menial and ratio scales. And then, we show AHP can be identify the attractiveness of each segment, in which the decision maker can select the best target segment.

• IE interfaces
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• v.20 no.4
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• pp.448-457
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• 2007

The small & micro business has the characteristics of both consumer credit risk and business credit risk. In predicting the bankruptcy for small-micro businesses, the problem is that in most cases, the financial data for evaluating business credit risks of small & micro businesses are not available. To alleviate such problem, we propose a bankruptcy prediction mechanism using the credit card sales information available, because most small businesses are member store of some credit card issuers, which is the main purpose of this study. In order to perform this study, we derive some variables and analyze the relationship between good and bad signs. We employ the new statistical learning technique, support vector machines (SVM) as a classifier. We use grid search technique to find out better parameter for SVM. The experimental result shows that credit card sales information could be a good substitute for the financial data for evaluating business credit risk in predicting the bankruptcy for small-micro businesses. In addition, we also find out that SVM performs best, when compared with other classifiers such as neural networks, CART, C5.0 multivariate discriminant analysis (MDA), and logistic regression.

• Journal of the Ergonomics Society of Korea
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• v.18 no.1
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• pp.13-24
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• 1999

The objective of the study is to discriminate EEG(electroencephalogram) due to emotional changes. Emotion was evoked by the series of auditory stimuli which were selected from the natural sounds in the sound effect collection of compact disc. Seventeen university students participated and experienced positive or negative emotions by six auditory stimuli with intermission between stimuli. Temporal EEG ($T_3$, $T_4$, $T_5$, and $T_6$) was recorded at the same time and a subjective test was performed on the eleven point scales after the experiment. The maximum and minimum scores of the EEG among six stimuli EEG were analyzed for discrimination of emotion. The EEG signals were transformed into feature objects based on scalar intervention model coefficients. Auditory stimulus was considered as intervention variable. They were classified by Discriminant Analysis for each channel. The features showed results with the best classification accuracy of 91.2 % in $T_4$ for auditory stimuli. This study could be extended to establish an algorithm which quantifies and classifies emotions evoked by auditory stimulus using time-series models.

• Journal of Korean Academy of Nursing
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• v.35 no.7
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• pp.1410-1419
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• 2005

Purpose: This study identified and compared influential variables on intention and action to quit smoking between adolescent smokers and adult smokers. Methods: For the selection of variables, the transtheoretical theory was used. A survey was conducted with 376 adolescent smokers in 4 high schools and 451 adult smokers in community settings in South Korea. Discriminant analysis was used for data analysis Results: The variables of adolescent smokers that predicted an intention to quit smoking were: smoking temptation, self re-evaluation, counter conditioning and stimulus control. The variables that predicted an action to quit smoking were: self-efficacy for smoking abstinence, pros for smoking, self reevaluation, and self liberation. The variables of adult smokers that predicted an intention to quit were: smoking temptation, pros for smoking, cons for smoking, self reevaluation. The variables that self liberation and predicted an action to quit smoking were: self efficacy for smoking abstinence, smoking temptation, and counter conditioning. Conclusions: Developing stage specific smoking intervention methods based on different ways of how individuals make a decision to quit smoking within their contexts needs to be done.