• Journal of Korea Entertainment Industry Association
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• v.13 no.4
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• pp.27-37
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• 2019

This study empirically identified and sought implications for the degree of desire by film crews to participate in overseas production and the factors affecting them, based on the recognition that the active participation of skilled film crews in overseas production and joint ventures can be an alternative to the poor treatment of domestic film industry sites and the reduction of job losses due to problems with career barriers. In order for respondents to verify research questions, binomial logistic and marginal effects analysis using SPSS and STATA, the data obtained by surveying 402 film crews operating in various areas. The notable findings are as follow: First, about 65%(263 people) of the respondents expressed their desire to participate in overseas activity, indicating that a considerable number of film crews are looking forward to opportunities with overseas productions. Second, major factors that have a positive effect on film crews' need to participate in overseas activities have been found to be professionality, artistic identity, Korean Wave persistence, and career barriers. In other words, the higher the professionality, the more clearly the identity of the artist, the more positive expectations of the possibility of the Korean Wave continuing, and the more serious the problem of career disability, the clearer was the desire to participate in overseas production and joint-work activities.

• 한국노년학
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• v.30 no.4
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• pp.1145-1161
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• 2010

The objectives of this study were to examine how social networks of old retirees impact on their retirement satisfaction, and through this, to suggest ways of improving their retirement satisfaction. Data used in this study were from 2006 KLoSA(Korean Longitudinal Study of Ageing), which were collected from 1,009 elderly people aged 65 and over who resided metropolis and smaller medium cities and answered regarding their retirement satisfaction. Data were analyzed by Binary Logistic Regression method. As a result, the frequency of contact with children, the number of participation in their social activities, and the satisfaction of relationship with children were the significant variables to predict retirement satisfaction. In addition, other variables such as gender, subjective health status, type of retirement, and duration of past retirement have been found as significant variables to explain retirement satisfaction. Implications for designing effective retirement plan and service systems have been discussed.

• Journal of the Health Care and Life Science
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• v.10 no.2
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• pp.305-314
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• 2022

The purpose of this study is to investigate the effect of health behavioral factors such as general characteristics, lifestyle and disease characteristics on depression in patients with chronic diseases. To this end, among 7,359 people who participated in the 8th National Health and Nutrition Survey conducted from 2019 to 2020, chi-square test analysis between health behavior factors and depression for 1980 people aged 19 years or older with chronic diseases and no missing values in the basic survey items was performed. After that, binary logistic regression analysis was performed with the factors that were significant as independent variables. As a result of the analysis, depression was 1.49 times higher in women than men (CI: 1.086~2.044), and was 1.828 higher in smokers than in non-smokers (CI: 1.285~2.561). And the higher the income level, the lower the depression. In particular, the odds ratio was 28.034 (CI: 13.132~59.849) in 'not stressful' versus 'very stressed', which had the greatest effect when the intensity of stress was very high. And the influence of subjective health cognition and sleeping hours was also relatively high. This study is meaningful in that it identified the priority of health behavior factors that should be practiced to improve depression in patients with chronic diseases. And since the number of comorbidity was not significant in the occurrence of depression, it would be necessary to identify the extent to which each type of chronic disease affects depression and to suggest policy alternatives tailored to each patient group.

• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.89-95
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• 2015

Traditional frequent pattern mining discovers valid patterns with no smaller frequency than a user-defined minimum threshold from databases. In this framework, an enormous number of patterns may be extracted by a too low threshold, which makes result analysis difficult, and a too high one may generate no valid pattern. Setting an appropriate threshold is not an easy task since it requires the prior knowledge for its domain. Therefore, a pattern mining approach that is not based on the domain knowledge became needed due to inability of the framework to predict and control mining results precisely according to the given threshold. Top-k frequent pattern mining was proposed to solve the problem, and it mines top-k important patterns without any threshold setting. Through this method, users can find patterns from ones with the highest frequency to ones with the k-th highest frequency regardless of databases. In this paper, we provide knowledge both on frequent and top-k pattern mining. Although top-k frequent pattern mining extracts top-k significant patterns without the setting, it cannot consider both item quantities in transactions and relative importance of items in databases, and this is why the method cannot meet requirements of many real-world applications. That is, patterns with low frequency can be meaningful, and vice versa, in the applications. High utility pattern mining was proposed to reflect the characteristics of non-binary databases and requires a minimum threshold. Recently, top-k high utility pattern mining has been developed, through which users can mine the desired number of high utility patterns without the prior knowledge. In this paper, we analyze two algorithms related to top-k high utility pattern mining in detail. We also conduct various experiments for the algorithms on real datasets and study improvement point and development direction of top-k high utility pattern mining through performance analysis with respect to the experimental results.

• The Korean Society of Law and Medicine
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• v.19 no.2
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• pp.235-261
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• 2018

The Medical law stipulates regulations about the physician's duty to inform to contribute to patient's self-determination. This law was most recently revised on December 20, 2016, and came into effect on June 21, 2017. There has been much controversy about this, and it has been questioned whether or not it will be effective for physicians to comply with the duty to inform. Therefore, this study investigated perceptions of physicians of whether they observed the duty to inform and their legal judgment about that duty, and analyzed how the revision of the medical law may have affected the legal cognition of physician's duty to inform. This study was conducted through an online questionnaire survey involving 109 physicians over 2 weeks from March 29 to April 12, 2018, and 108 of the collected data were used for analysis. The questionnaire was developed by revising and supplementing the previous research (Lee, 2004). It consisted of 41 items, including 26 items related to the experience of and legal judgment about the duty to inform, 6 items related to awareness of revised medical law, and 9 items on general characteristics. The data were analyzed using SAS 9.4 program and descriptive statistics, Chi-square test, Fisher's exact test and Binary logistic regression were performed. The results are as follows. • Out of eight situations, the median number of situations that did not fulfill the duty to inform was 5 (IQR, 4-6). In addition, 12 respondents (11%) answered that they did not fulfill the duty to inform in all eight cases, while only one (1%) responded that he/she performed explanation obligations in all cases. • The median number of the legal judgment score on the duty to inform was 8 out of 13 (IQR, 7-9), and the scores ranged from a minimum of 4 (4 respondents) to a maximum of 11 (3 respondents). • More than half of the respondents (n=26, 52%) were unaware of the revision of the medical law, 27 (25%) were aware of the fact that the medical law had been revised, 20(18%) had a rough knowledge of the contents of the law, and only 5(5%) said they knew the contents of the law in detail. The level of awareness of the revised medical law was statistically significant difference according to respondents' sex (p<.49), age (p<.0001), career (p<.0001), working type (p<.024), and department (p<.049). • There was no statistically significant relationship between the level of awareness of the revised medical law and the level of legal judgment on the duty to inform. These results suggest that efforts to improve the implementation and cognition of physician's duty to inform are needed, and it is difficult to expect a direct positive effect from the legal regulations per se. Considering the distinct characteristics of medical institutions and hierarchical organizational culture of physicians, it is necessary to develop a credible guideline on the duty to inform within the medical system, and to strengthen the education of physicians about their duty to inform and its purpose.

• Journal of agricultural medicine and community health
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• v.48 no.2
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• pp.103-117
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• 2023

Objectives: This study aims to reveal the relationship between social relations and physical activity in the young-old and old-old elderly. Methods: Data from 2020 National survey of Older Koreans were used, and a total of 10,097 subjects over the age of 65 were included in analysis. The dependent variable was physical activity, and the independent variables were social relations barrier and motivational factors. x²-test and binary logistic regression were performed for data analysis. Results: The physical activity rate in the elderly were 40.8% in the young-old and 29.2% in the old-old. The socio-demographic characteristics affecting physical activity were the young-old elderly were sex, residential area, employment status and household income, and the old-old elderly were sex, age, residential area, education level and household income. The social relations barrier factors affecting physical activity were the young-old elderly were number of close friends, family care, exercise information search and video viewing, and the old-old elderly were household type, number of close friends, participation in exercise education, exercise information search and video viewing. The social relations motivational factors affecting physical activity were the young-old elderly were call with children/relative/friend, participation in sports activity, access time from home to parks, and the old-old elderly were call with children/relative/friend, participation in sports activity, satisfaction with green spaces. Conclusions: It was found that social relations barrier and motivational factors of the elderly are important factors to consider when developing physical activity promotion strategy, and there are also difference between the age of the elderly.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.57-79
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• 2016

It is a rising trend that the number of users using one of the social media channels, the Social Network Service, so called the SNS, is getting increased. As per to this social trend, more companies have interest in this networking platform and start to invest their funds in it. It has received much attention as a tool spreading and expanding the message that a company wants to deliver to its customers and has been recognized as an important channel in terms of the relationship marketing with them. The environment of media that is radically changing these days makes possible for companies to approach their customers in various ways. Particularly, the social network service, which has been developed rapidly, provides the environment that customers can freely talk about products. For companies, it also works as a channel that gives customized information to customers. To succeed in the online environment, companies need to not only build the relationship between companies and customers but focus on the relationship between customers as well. In response to the online environment with the continuous development of technology, companies have tirelessly made the novel marketing strategy. Especially, as the one-to-one marketing to customers become available, it is more important for companies to maintain the relationship marketing with their customers. Among many SNS, Facebook, which many companies use as a communication channel, provides a fan page service for each company that supports its business. Facebook fan page is the platform that the event, information and announcement can be shared with customers using texts, videos, and pictures. Companies open their own fan pages in order to inform their companies and businesses. Such page functions as the websites of companies and has a characteristic of their brand communities such as blogs as well. As Facebook has become the major communication medium with customers, companies recognize its importance as the effective marketing channel, but they still need to investigate their business performances by using Facebook. Although there are infinite potentials in Facebook fan page that even has a function as a community between users, which other platforms do not, it is incomplete to regard companies' Facebook fan pages as communities and analyze them. In this study, it explores the relationship among customers through the network of the Facebook fan page users. The previous studies on a company's Facebook fan page were focused on finding out the effective operational direction by analyzing the use state of the company. However, in this study, it draws out the structural variable of the network, which customer committment can be measured by applying the social network analysis methodology and investigates the influence of the structural characteristics of network on the business performance of companies in an empirical way. Through each company's Facebook fan page, the network of users who engaged in the communication with each company is exploited and it is the one-mode undirected binary network that respectively regards users and the relationship of them in terms of their marketing activities as the node and link. In this network, it draws out the structural variable of network that can explain the customer commitment, who pressed "like," made comments and shared the Facebook marketing message, of each company by calculating density, global clustering coefficient, mean geodesic distance, diameter. By exploiting companies' historical performance such as net income and Tobin's Q indicator as the result variables, this study investigates influence on companies' business performances. For this purpose, it collects the network data on the subjects of 54 companies among KOSPI-listed companies, which have posted more than 100 articles on their Facebook fan pages during the data collection period. Then it draws out the network indicator of each company. The indicator related to companies' performances is calculated, based on the posted value on DART website of the Financial Supervisory Service. From the academic perspective, this study suggests a new approach through the social network analysis methodology to researchers who attempt to study the business-purpose utilization of the social media channel. From the practical perspective, this study proposes the more substantive marketing performance measurements to companies performing marketing activities through the social media and it is expected that it will bring a foundation of establishing smart business strategies by using the network indicators.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.