• 산업혁신연구
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• v.35 no.3
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• pp.25-64
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• 2019

Organizational change is an important organizational phenomenon that can determine the survival and failure of an organization. Organizational effort to respond actively to the pressures surrounding an organization is regarded as a core decision-making area of the organization. Indeed, a variety of efforts have been made to achieve organizational change, with separate academic disciplines such as organizational development and organizational change being established and presented in various organizational change phase models. However, despite these efforts to lead organizational change, success rate of it is only 20% to 25% and most of it fails. One of the key issues pointed out in the various studies that have identified the causes of organizational change failures is the degree of readiness for organizational change. In this regard, readiness for organizational change is regarded to be essential to ensure achievement of organizational change. Prior to increase readiness for organizational change, precise measure the organizational readiness of the current organization is required. By measuring it, organization can identify the areas that need to be supplemented and fulfilled to introduce and implement organizational change. The goal of this research is to review the developed scales for measuring organizational change readiness and to grasp the overall characteristics of them, by dividing into technical characteristics and psychometric properties. To review the scales, a systematic literature review on the studies was conducted.Review indicated that the existing scales were limited to the specific dimension in the conceptual realm of organizational change readiness. In addition, psychometric properties also showed low reliability and validity. Therefore, when measuring the organizational readiness level for domestic organizations, it is necessary to use a scale with a relatively high psychometric basis while pre-specifying the measurement domain (psychological dimension/structure dimension) in advance. In addition, it is required to develop a scale of organizational change readiness in the context of domestic organization with high reliability and validity basis, while taking into consideration the comprehensive concept of organizational change readiness which is being discussed recently.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Tuberculosis and Respiratory Diseases
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• v.53 no.4
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• pp.401-408
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• 2002

Background : Tuberculin skin test is a method to examine M. tuberculosis infection and has been used all over the world. But various factors make it difficult to understand testing results. In 2000, the American Thoracic Society recommended that skin test results should be decided by considering risk factors of the tested. In Korea, high tuberculosis infection rate and BCG vaccination rate make it difficult to differentiate current infection, past infection, and no infection by the skin test. This study was attempted to examine a negative predictive value of the skin test to understand how the skin test acts on deciding administration of anti-tuberculosis drug. Methods : From Mar. 1 to Jul. 31 in 2001, the test was performed for patients hospitalized in Department of Internal Medicine, Hallym University College of Medicine, Chunchon, Korea by administering Tuberculin PPD RT23 2 TU (0.1 ml)to them that has been currently used in Korea based on Mantoux method. They were decided to be infected with tuberculosis bacilli by following diagnostic standard: 1) tuberculosis bacilli was cultured in sputum by microbiological diagnostic standard or Acid-fast bacilli was proven on a microscopic examination or 2) tuberculosis bacilli was not proven in the aforesaid microbiological test by clinical diagnostic standard, while there was opinion or symptom suitable for tuberculosis by radiographic or histological standard so the doctor decided to apply the tuberculosis treatment. Results : In this study, total 210 patients except 20 patients (8.7%) among 230 hospitalized patients were evaluated. Their average age was 60±16.8 years, and male-female rate was 1.28 : 1 (male: 118, female: 92). Number of patient, who was diagnosed and decided as tuberculosis, was 53(25.2%). Pulmonary tuberculosis was found in 45 patients (84.9%); 22 patients were decided to be positive in the Acid-fast bacilli smear test by microbiological examination (culture positive: 13, culture negative: 9), and 23 patients were decided to be tuberculosis patients by clinical diagnosis standard. Tuberculosis pleuritis was found in 8 patients (15.1%); 4 patients were diagnosed and decided by histological standard, and 4 patients were decided and treated by clinical standard. In differentiating patients into 'Negative' and 'Positive' by the skin test standard of the American Thoracic Society, negative predictive value 92.3%, positive predictive value 47.3%, sensitivity and specificity were 83%, 68.8%, respectively. Conclusion : In hospitalized respiratory patients, there was high negative predictive vlaue 92.3% by tuberculin skin test, therefore skin test would be a important factor for deciding administration of anti-tuberculosis drug on negative skin test patient.

• Radiation Oncology Journal
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• v.23 no.2
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• pp.61-70
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• 2005

Purpose: Uterine cervix cancer is one of the most prevalent women cancer in Korea. We analysed published papers in Korea with comparing Patterns of Care Study (PCS) articles of United States and Japan for the purpose of developing and processing Korean PCS. Materials and Methods: We searched PCS related foreign-produced papers in the PCS homepage (212 articles and abstracts) and from the Pub Med to find Structure and Process of the PCS. To compare their study with Korean papers, we used the internet site 'Korean Pub Med' to search 99 articles regarding uterine cervix cancer and radiation therapy. We analysed Korean paper by comparing them with selected PCS papers regarding Structure, Process and Outcome and compared their items between the period of before 1980's and 1990's. Results: Evaluable papers were 28 from United States, 10 from the Japan and 73 from the Korea which treated cervix PCS items. PCS papers for United States and Japan commonly stratified into $3\~4$ categories on the bases of the scales characteristics of the facilities, numbers of the patients, doctors, Researchers restricted eligible patients strictly. For the process of the study, they analysed factors regarding pretreatment staging in chronological order, treatment related factors, factors in addition to FIGO staging and treatment machine. Papers in United States dealt with racial characteristics, socioeconomic characteristics of the patients, tumor size (6), and bilaterality of parametrial or pelvic side wail invasion (5), whereas papers from Japan treated of the tumor markers. The common trend in the process of staging work-up was decreased use of lymphangiogram, barium enema and increased use of CT and MRI over the times. The recent subject from the Korean papers dealt with concurrent chemoradiotherapy (9 papers), treatment duration (4), tumor markers (B) and unconventional fractionation. Conclusion: By comparing papers among 3 nations, we collected items for Korean uterine cervix cancer PCS. By consensus meeting and close communication, survey items for cervix cancer PCS were developed to measure structure, process and outcome of the radiation treatment of the cervix cancer. Subsequent future research will focus on the use of brachytherapy and its impact on outcome including complications. These finding and future PCS studies will direct the development of educational programs aimed at correcting identified deficits in care.

• Information Systems Review
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• v.14 no.3
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• pp.75-97
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• 2012

Fostering trusting belief in financial transactions is a challenging task in Internet banking services. Authenticated Certificate had been regarded as an effective method to guarantee the trusting belief for online transactions. However, previous research claimed that this method has some loopholes for such abusers as hackers, who intend to attack the financial accounts of innocent transactors in Internet. Two types of methods have been suggested as alternatives for securing user identification and activity in online financial services. Control transparency uses information over the transaction process to verify and to control the transactions. Outcome feedback, which refers to the specific information about exchange outcomes, provides information over final transaction results. By using these two methods, financial service providers can send signals to involved parties about the robustness of their security mechanisms. These two methods-control transparency and outcome feedback-have been widely used in the IS field to enhance the quality of IS services. In this research, we intend to verify that these two methods can also be used to reduce risks and to increase the security protections in online banking services. The purpose of this paper is to empirically test the effects of the control transparency and the outcome feedback on the risk perceptions in Internet banking services. Our assumption is that these two methods-control transparency and outcome feedback-can reduce perceived risks involved with online financial transactions, while increasing perceived trust over financial service providers. These changes in user attitudes can increase the level of user satisfactions, which may lead to the increased user loyalty as well as users' willingness to pay for the financial transactions. Previous research in IS suggested that the increased level of transparency on the process and the result of transactions can enhance the information quality and decision quality of IS users. Transparency helps IS users to acquire the information needed to control the transaction counterpart and thus to complete transaction successfully. It is also argued that transparency can reduce the perceived transaction risks in IS usage. Many IS researchers also argued that the trust can be generated by the institutional mechanisms. Trusting belief refers to the truster's belief for the trustee to have attributes for being beneficial to the truster. Institution-based trust plays an important role to enhance the probability of achieving a successful outcome. When a transactor regards the conditions crucial for the transaction success, he or she considers the condition providers as trustful, and thus eventually trust the others involved with such condition providers. In this process, transparency helps the transactor complete the transaction successfully. Through the investigation of these studies, we expect that the control transparency and outcome feedback can reduce the risk perception on transaction and enhance the trust with the service provider. Based on a theoretical framework of transparency and institution-based trust, we propose and test a research model by evaluating research hypotheses. We have conducted a laboratory experiment in order to validate our research model. Since the transparency artifact(control transparency and outcome feedback) is not yet adopted in online banking services, the general survey method could not be employed to verify our research model. We collected data from 138 experiment subjects who had experiences with online banking services. PLS is used to analyze the experiment data. The measurement model confirms that our data set has appropriate convergent and discriminant validity. The results of testing the structural model indicate that control transparency significantly enhances the trust and significantly reduces the risk perception of online banking users. The result also suggested that the outcome feedback significantly enhances the trust of users. We have found that the reduced risk and the increased trust level significantly improve the level of service satisfaction. The increased satisfaction finally leads to the increased loyalty and willingness to pay for the financial services.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Journal of Korean Neurosurgical Society
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• v.30 no.10
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• pp.1210-1219
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• 2001

Introduction : The purpose of this study was to analyze the safety, pullout strength and radiographic characteristics of unicortical and bicortical screws of cervical facet within cadaveric specimens and evaluate the influence of level of training on the positioning of these screws. Methods : Twenty-one cadavers, mean 78.9 years of age, underwent bilateral placement of 3.5mm AO lateral mass screw from C3-C6(n=168) using a slight variation of the Magerl technique. Intraoperative imaging was not used. The right side(unicortical) utilized only 14mm screws(effective length of 11mm) while on the left side to determine the length of the screw after the ventral cortex had been drilled. Three spine surgeons(attending, fellow, chief resident) with varying levels of spine training performed the procedure on seven cadavers each. All spines were harvested and lateral radiographs were taken. Individual cervical vertebrae were carefully dissected and then axial radiographs were taken. The screws were evaluated clinically and radiographically for their safety. Screws were graded clinically for their safety with respect to the spinal cord, facet joint, nerve root and vertebral artery. The grades consisted of the following categories : "satisfactory", "at risk" and "direct injury". Each screw was also graded according to its zone placement. Screw position was quantified by measuring a sagittal angle from the lateral radiograph and an axial angle from the axial radiograph. Pull-out force was determined for all screws using a material testing machine. Results : Dissection revealed that fifteen screws on the left side actually had only unicortical and not bicortical purchase as intended. The majority of screws(92.8%) were satisfactory in terms of safety. There were no injuries to the spinal cord. On the right side(unicortical), 98.9% of the screws were "satisfactory" and on the left side(bicortical) 68.1% were "satisfactory". There was a 5.8% incidence of direct arterial injury and a 17.4% incidence of direct nerve root injury with the bicortical screws. There were no "direct injuries" with the unicortical screws for the nerve root or vertebral artery. The unicortical screws had a 21.4% incidence of direct injury of the facet joint, while the bicortical screws had a 21.7% incidence. The majority of "direct injury" of bicortical screws were placed by the surgeon with the least experience. The performance of the resident surgeon was significantly different from the attending or fellow(p<0.05) in terms of safety of the nerve root and vertebral artery. The attending's performance was significantly better than the resident or fellow(p<0.05) in terms of safety of the facet joint. There was no relationship between the safety of a screw and its zone placement. The axial deviation angle measured $23.5{\pm}6.6$ degrees and $19.8{\pm}7.9$ degrees for the unicortical and bicortical screws, respectively. The resident surgeon had a significantly lower angle than the attending or fellow(p<0.05). The sagittal angle measured $66.3{\pm}7.0$ degrees and $62.3{\pm}7.9$ degrees for the unicortical and bicortical screws, respectively. The attending had a significantly lower sagittal angle than the fellow or resident(p<0.05). Thirty-three screws that entered the facet joint were tested for pull-out strength but excluded from the data because they were not lateral mass screws per-se and had deviated substantially from the intended final trajectory. The mean pull-out force for all screws was $542.9{\pm}296.6N$. There was no statistically significant difference between the pull-out force for unicortical($519.9{\pm}286.9N$) and bicortical($565.2{\pm}306N$) screws. There was no significant difference in pull-out strengths with respect to zone placement. Conclusion : It is our belief that the risk associated with bicortical purchase mandates formal spine training if it is to be done safely and accurately. Unicortical screws are safer regardless of level of training. It is apparent that 14mm lateral mass screws placed in a supero-lateral trajectory in the adult cervical spine provide an equivalent strength with a much lower risk of injury than the longer bicortical screws placed in a similar orientation.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.17-35
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• 2015

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase for a certain period of time. Developing precise forecasting models are considered important since corporates can make strategic decisions on new markets based on future demand estimated by the models. Many studies have developed market growth curve models, such as Bass, Logistic, Gompertz models, which estimate future demand when a market is in its early stage. Among the models, Bass model, which explains the demand from two types of adopters, innovators and imitators, has been widely used in forecasting. Such models require sufficient demand observations to ensure qualified results. In the beginning of a new market, however, observations are not sufficient for the models to precisely estimate the market's future demand. For this reason, as an alternative, demands guessed from those of most adjacent markets are often used as references in such cases. Reference markets can be those whose products are developed with the same categorical technologies. A market's demand may be expected to have the similar pattern with that of a reference market in case the adoption pattern of a product in the market is determined mainly by the technology related to the product. However, such processes may not always ensure pleasing results because the similarity between markets depends on intuition and/or experience. There are two major drawbacks that human experts cannot effectively handle in this approach. One is the abundance of candidate reference markets to consider, and the other is the difficulty in calculating the similarity between markets. First, there can be too many markets to consider in selecting reference markets. Mostly, markets in the same category in an industrial hierarchy can be reference markets because they are usually based on the similar technologies. However, markets can be classified into different categories even if they are based on the same generic technologies. Therefore, markets in other categories also need to be considered as potential candidates. Next, even domain experts cannot consistently calculate the similarity between markets with their own qualitative standards. The inconsistency implies missing adjacent reference markets, which may lead to the imprecise estimation of future demand. Even though there are no missing reference markets, the new market's parameters can be hardly estimated from the reference markets without quantitative standards. For this reason, this study proposes a case-based expert system that helps experts overcome the drawbacks in discovering referential markets. First, this study proposes the use of Euclidean distance measure to calculate the similarity between markets. Based on their similarities, markets are grouped into clusters. Then, missing markets with the characteristics of the cluster are searched for. Potential candidate reference markets are extracted and recommended to users. After the iteration of these steps, definite reference markets are determined according to the user's selection among those candidates. Then, finally, the new market's parameters are estimated from the reference markets. For this procedure, two techniques are used in the model. One is clustering data mining technique, and the other content-based filtering of recommender systems. The proposed system implemented with those techniques can determine the most adjacent markets based on whether a user accepts candidate markets. Experiments were conducted to validate the usefulness of the system with five ICT experts involved. In the experiments, the experts were given the list of 16 ICT markets whose parameters to be estimated. For each of the markets, the experts estimated its parameters of growth curve models with intuition at first, and then with the system. The comparison of the experiments results show that the estimated parameters are closer when they use the system in comparison with the results when they guessed them without the system.

• The Korean Society of Law and Medicine
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• v.17 no.1
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• pp.299-346
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• 2016

There were also various decisions made in medical area in 2015. In the case that an inmate in a sanatorium was injured due to the reason which can be attributable to the sanatorium and the social welfare foundation that operates the sanatorium request treatment of the patient, the court set the standard of fixation of a party in medical contract. In the case that the family of the patient who was declared brain dead required withdrawal of meaningless life sustaining treatment but the hospital rejected and continued the treatment, the court made a decision regarding chargeable fee for such treatment. When it comes to the eye brightening operation which received measure of suspension from the Ministry of Health and Welfare for the first time in February, 2011, because of uncertainty of its safety, the court did not accept the illegality of such operation itself, however, ordered compensation of the whole damage based on the violation of liability for explanation, which is the omission of explanation about the fact that the cost-effectiveness is not sure as it is still in clinical test stage. There were numerous cases that courts actively acknowledged malpractices; in the cases of paresis syndrome after back surgery, quite a few malpractices during the surgery were acknowledged by the court and in the case of nosocomial infection, hospital's negligence to cause such nosocomial infection was acknowledged by the court. There was a decision which acknowledged malpractice by distinguishing the duty of installation of emergency equipment according to the Emergency Medical Service Act and duty of emergency measure in emergency situations, and a decision which acknowledged negligence of a hospital if the hospital did not take appropriate measures, although it was a very rare disease. In connection with the scope of compensation for damage, there were decisions which comply with substantive truth such as; a court applied different labor ability loss rate as the labor ability loss rate decreased after result of reappraisal of physical ability in appeal compared to the one in the first trial, and a court acknowledged lower labor ability loss rate than the result of appraisal of physical ability considering the condition of a patient, etc. In the event of any damage caused by malpractice, in regard to whether there is a limitation on liability in fee charge after such medical malpractice, the court rejected the hospital's claim for setoff saying that if the hospital only continued treatments to cure the patient or prevent aggravation of disease, the hospital cannot charge Medical bills to the patient. In regard to the provision of the Medical Law that prohibit medical advertisement which was not reviewed preliminarily and punish the violation of such, a decision of unconstitutionality was made as it is a precensorship by an administrative agency as the deliberative bodies such as Korean Medical Association, etc. cannot be denied to be considered as administrative bodies. When it comes to the issue whether PRP treatment, which is commonly performed clinically, should be considered as legally determined uninsured treatment, the court made it clear that legally determined uninsured treatment should not be decided by theoretical possibility or actual implementation but should be acknowledged its medical safety and effectiveness and included in medical care or legally determined uninsured treatment. Moreover, court acknowledged the illegality of investigation method or process in the administrative litigation regarding evaluation of suitability of sanatorium, however, denied the compensation liability or restitution of unjust enrichment of the Health Insurance Review & Assessment Service and the National Health Insurance Corporation as the evaluation agents did not cause such violation intentionally or negligently. We hope there will be more decisions which are closer to substantive truth through clear legal principles in respect of variously arisen issues in the future.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.