• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.83-93
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• 2001

Prediction of corporate failure using past financial data is well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks (NNs) can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. Although numerous theoretical and experimental studies reported the usefulness or neural networks in classification studies, there exists a major drawback in building and using the model. That is, the user can not readily comprehend the final rules that the neural network models acquire. We propose a genetic algorithms (GAs) approach in this study and illustrate how GAs can be applied to corporate failure prediction modeling. An advantage of GAs approach offers is that it is capable of extracting rules that are easy to understand for users like expert systems. The preliminary results show that rule extraction approach using GAs for bankruptcy prediction modeling is promising.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.4
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• pp.55-70
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• 2001

Artificial neural network(ANN) models have been widely used for the classification problems in business such as bankruptcy prediction, credit evaluation, etc. Although the application of ANN to classification of consumers＇ choice behavior is a promising research area, there have been only a few researches. In general, most of the researches have reported that the classification performance of the ANN models were better than conventional statistical model Because the survey data on consumer behavior may include much noise and missing data, ANN model will be more robust than conventional statistical models welch need various assumptions. The purpose of this paper is to study the potential of the ANN model for forecasting consumers＇ choice behavior based on survey data. The data was collected by questionnaires to the shoppers of department stores and discount stores. Then the correct classification rates of the ANN models for the training and test sample with that of multiple discriminant analysis(MDA) and logistic regression(Logit) model. The performance of the ANN models were betted than the performance of the MDA and Logit model with respect to correct classification rate. By using input variables identified as significant in the stepwise MDA, the performance of the ANN models were improved.

• 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.

• 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.

• The korean journal of orthodontics
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• v.39 no.2
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• pp.112-119
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• 2009

Objective: To develop a mixed dentition analysis method in consideration of the normal variation of tooth sizes. Methods: According to the tooth-size of the maxillary central incisor, maxillary 1st molar, mandibular central incisor, mandibular lateral incisor, and mandibular 1st molar, 307 normal occlusion subjects were clustered into the smaller and larger tooth-size groups. Multiple regression analyses were then performed to predict the sizes of the canine and premolars for the 2 groups and both genders separately. For a cross validation dataset, 504 malocclusion patients were assigned into the 2 groups. Then multiple regression equations were applied. Results: Our results show that the maximum errors of the predicted space for the canine, 1st and 2nd premolars were 0.71 and 0.82 mm residual standard deviation for the normal occlusion and malocclusion groups, respectively. For malocclusion patients, the prediction errors did not imply a statistically significant difference depending on the types of malocclusion nor the types of tooth-size groups. The frequency of prediction error more than 1 mm and 2 mm were 17.3% and 1.8%, respectively. The overall prediction accuracy was dramatically improved in this study compared to that of previous studies. Conclusions: The computer aided calculation method used in this study appeared to be more efficient.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1128-1135
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• 2011

The purpose of this study was to identify the effects of field athletes' self-management on their psychological happiness. The subjects of the study were 184 athletes (male=82, female=102) who participated in the 91th National Athletes Meeting. All statistical analyses and description methods were computed by SPSS window 18.0. The discriminant analysis was used to find effects of self-management on their psychological happiness, ANOVA and Multiple regression. The results of this study were as followings: Frist, there was not significant difference to The Relationship between social-demographical variables and self-management, their psychological happiness. Second, self-management had a significant effect on psychological happiness. Based on the results, Field athletes's self-management has a positive effect on their psychological happiness.

• Journal of Korean Society of Forest Science
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• v.105 no.2
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• pp.157-166
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• 2016

This study was carried out to classify the current forest cover types and to propose the successional trends in the natural forest of Mt. Deogyu. The vegetation data were collected by the point-centered quarter method. The forest cover types were classified by various multivariate statistical analysis methods such as cluster analysis, indicator species analysis and multiple discriminant analysis. This forests were classified into five forest types by the species composition of upper layer and topographic positions: Quercus mongolica forest in the ridge, Fraxinus mandushurica-F. rhynchophylla-Cornus controversa forest and F. mandushurica forest in the valley, the Q. serrata - Pinus densiflora - Q. mongolica forest and P. densiflora forest in the low-slope. As a result of the forest successional trends depending on ecological and environmental characteristics in each forest type, the current forest types were expected that the forest succession would be proceeded toward Q. mongolica forest, F. mandshurica forest, mixed mesophytic forest, and oak-Carpinus laxiflora forest.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.8 no.1
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• pp.69-80
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• 2001

This study was done to investigate the relation of smoking temptation, stage of change in smoking cessation, nicotine dependency and perceived health status in middle aged men. Convenience samples of 176 subjects who were either smoked or used to smoke, aged between 30 to 64, living in Seoul and Kyungi province area in Korea were selected for the study. The data was collected from December 1, 1999 to June 30, 2000. The research instrument were Stage of Change of Smoking Cessation Measure (DiClemente et al. 1991). Smoking Temptation Measure (Velicer, DiClemente, Rossi, Prochaska. 1990), Perceived Health Status Measure (McDowell & Newell, 1996), and Nicotine Dependency Scale (FTQ: Fagerstrom, 1978). The data were analyzed using the SAS Program. The result of the study are as follows : 1. The analysis of variance and multiple comparison showed that according to the stage of change, there were significant mean differences in the three sub-factors of smoking temptation; 'positive affect situation (F=12.64, p=.0001)', 'negative affect situation (F=16.01, p=.0001)', 'habitual craving situation (F=14.43, p=.0001)' and nicotine dependency (F=4.12, p=.0033) The mean score for smoking temptation for the subjects who were in the precontemplation stage outweighed the mean score for smoking temptation for subjects who were in the maintenance stage. 2. Through discriminant analysis, it was found that negative affect situation was the most influential variable of the smoking temptation sub-factors which can be used to discriminate stage of change. 3. The analysis of Pearson correlation coefficients showed that there was a significant positive relation between nicotine dependency and negative affect situation of smoking cessation((r=0.2182, p=0.0045) and a significant negative relation between nicotine dependency and perceived health status(r=-0.2115, p=0.0059).

• Journal of Ginseng Research
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• v.46 no.2
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• pp.290-295
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• 2022

Background: Dried and red ginseng are well-known types of processed ginseng and are widely used as healthy food. The dried and red ginseng quality may vary with the storage period of raw ginseng. Therefore, herein, the effect of the storage period of fresh ginseng on processed ginseng quality was evaluated through multicomponent quantification with statistical analysis. Methods: A method based on ultrahigh performance liquid chromatography coupled to triple quadrupole mass spectrometry in multiple-reaction monitoring mode (UPLC-MRM-MS) was developed for quantitation of ginsenosides and oligosaccharides in dried and red ginseng. Principal component analysis and partial least squares discriminant analysis were conducted to evaluate the dynamic distributions of ginsenosides and oligosaccharides after different storage periods. Results: Eighteen PPD, PPT and OLE ginsenosides and nine reducing and nonreducing oligosaccharides were identified and quantified. With storage period extension, the ginsenoside content in the processed ginseng increased slightly in the first 2 weeks and decreased gradually in the following 9 weeks. The content of reducing oligosaccharides decreased continuously as storage time extending, while that of the nonreducing oligosaccharides increased. Chemical conversions occurred during storage, based on which potential chemical markers for the storage period evaluation of fresh ginseng were screened. Conclusion: According to ginsenoside and oligosaccharide distributions, it was found that the optimal storage period was 2 weeks and that the storage period of fresh ginseng should not exceed 4 weeks at 0 ℃. This study provides deep insights into the quality control of processed ginseng and comprehensive factors for storage of raw ginseng.

• Journal of Korean Clinical Nursing Research
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• v.27 no.3
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• pp.279-293
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• 2021

Purpose: The purpose of this study was to develop a Korean nursing work environment scale for critical care nurses (KNWES-CCN) and verify its validity and reliability. Methods: A total of 46 preliminary items were selected using content validity analysis of experts on 64 candidate items derived through literature reviews and in-depth interviews with critical care nurses. 535 critical care nurses from 21 hospitals responded to the preliminary questionnaire from February to March 2021. The collected data were analysed using construct, convergent and discriminant validities, and internal consistency and test-retest reliability. Results: The 23 items in 4 factors accounted for 55.6% of the total variance were identified through item analysis and exploratory factor analysis (EFA). EFA was performed with maximum likelihood method including direct oblimin method. In the confirmatory factor analysis, KNWES-CCN consisted of 21 items in 4 factors by deleting the items that were not meet the condition that the factor loading over .50 or the squared multiple correlation over .30. This model was considered to be suitable because it satisfied the fit index and acceptable criteria of the model [𝒳²=440.47 (p<.001), CMIN/DF=2.41, GFI=.86, SRMR=.06, RMSEA=.07, TLI=.90, CFI=.91]. The item total correlation values ranged form .32 to .73 and its internal consistency was Cronbach's α=.92. The reliability of the test-retest correlation coefficient was .72 and the intra-class correlation coefficient was .83. Conclusion: The KNWES-CCN showed good validity and reliability. Therefore, it is expected that the use of this scale would measure and improve nursing work environment for critical care nurses in Korea.