• Korean Journal of Mineralogy and Petrology
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• v.35 no.1
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• pp.41-50
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• 2022

To study the properties of Sr²⁺ exchange into zeolite A with increasing the molar concentration of Na⁺ in given exchange solution, four single crystals of fully dehydrated Sr²⁺- and Na⁺- exchanged zeolite A were prepared by the bath method using mixed ion-exchange solutions. The Sr(NO₃)₂:NaNO₃ molar rations of the ion exchange solution were 1:1(crystal 1), 1:100(crystal 2), 1:250(crystal 3), and 1:500 (crystal 4), respectively, with a total concentration of 0.05 M. The single-crystals were then vacuum dehydration at 623 K and 1×10^-4 Pa for 2 days. Their single-crystal structures were determined by single-crystal synchrotron X-ray diffraction techniques in the cubic space group Pm3-m, at 100(1) K, and were then refined to the final error indices of R₁/wR₂=0.047/0.146, 0.048/0.142, 0.036/0.128, and 0.040/0.156 for crystals 1, 2, 3, and 4, respectively. In crystals 1 and 2, the 6 Sr²⁺ ions are found at three different crystallographic sites. In crystal 3, 1 Sr²⁺ and 10 Na⁺ ions are found in large cavity and sodalite unit. In crystal 4, only 12 Na⁺ ions occupy three equipoints. The degree of Sr²⁺ ion-exchange decreased sharply from 100 to 16.7 to 0% as the initial Na⁺ concentration increase and the Sr²⁺ concentration decrease. In addition, the unit cell constant of the zeolite framework decreased with this lower level of Sr²⁺ exchange.

• The Journal of Korean Society for Radiation Therapy
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• v.33
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• pp.89-97
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• 2021

Purpose: The purpose of this study is to compare and evaluate the dose change according to the gas volume variations in the rectum, which was not included in the treatment plan during radiation therapy for cervical cancer. Materials and methods: Static Intensity Modulated Radiation Therapy (S-IMRT) using a 9-field and Volumetric Modulated Arc Therapy (VMAT) using 2 full-arcs were established with treatment planning system on Computed Tomography images of a human phantom. Random gas parameters were included in the Planning Target Volume(PTV) with a maximum change of 2.0 cm in increments of 0.5 cm. Then, the Conformity Index (CI), Homogeneity Index (HI) and PTV D_max for the target volume were calculated, and the minimum dose (D_min), mean dose (D_mean) and Maximum Dose (D_max) were calculated and compared for OAR(organs at risk). For statistical analysis, T-test was performed to obtain a p-value, where the significance level was set to 0.05. Result: The HI coefficients of determination(R²) of S-IMRT and VMAT were 0.9423 and 0.8223, respectively, indicating a relatively clear correlation, and PTV D_max was found to increase up to 2.8% as the volume of a given gas parameter increased. In case of OAR evaluation, the dose in the bladder did not change with gas volume while a significant dose difference of more than D_mean 700 cGy was confirmed in rectum using both treatment plans at gas volumes of 1.0 cm or more. In all values except for D_mean of bladder, p-value was less than 0.05, confirming a statistically significant difference. Conclusion: In the case of gas generation not considered in the reference treatment plan, as the amount of gas increased, the dose difference at PTV and the dose delivered to the rectum increased. Therefore, during radiation therapy, it is necessary to make efforts to minimize the dose transmission error caused by a large amount of gas volumes in the rectum. Further studies will be necessary to evaluate dose transmission by not only varying the gas volume but also where the gas was located in the treatment field.

• Analytical Science and Technology
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• v.21 no.6
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• pp.487-494
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• 2008

An experiment was performed for zone refined Re-filament and normal (nonzone refined) Re-filament to reduce the background effect on the measurement of low level uranium samples. From both filaments, the signals which seemed to come from a cluster of light alkali elements, $(^{39}K_6)^+$, $(^{39}K_5+^{41}K)^+$ and $PbO_2$ were identified as the isobaric effect of the uranium isotopes. The isobaric effect signal was completely disappeared by heating the filament about $2000^{\circ}C$ at < $10^{-7}$ torr of vacuum for more than 1.5 hour in zone refined Refilaments, while that from the normal Re-filaments was not disappeared completely and was still remained as 3 pg. of uranium as the impurities after the degassing treatment was performed for more than 5 hours at the same condition of zone refined filaments. A threshold condition eliminating impurities were proved to be at 5 A and 30 minutes of degassing time. The uranium content as an impurity in rhenium filament was checked with a filament degassing treatment using the U-233 spike by isotope dilution mass spectrometry. A 0.31 ng of U was detected in rhenium filament without degassing, while only 3 pg of U was detected with baking treatment at a current of 5.5 A for 1 hr. Using normal Re-filaments for the ultra trace of uranium sample analysis had something problem because uranium remains to be 3 pg on the filament even though degassed for long hours. If the 1 ng uranium were measured, 0.3% error occurred basically. It was also conformed that ionization filament current was recommended not to be increased over 5.5 A to reduce the background. Finally, the contents of uranium isotopes in uranium standard materials (KRISS standard material and NIST standard materials, U-005 and U-030) were measured and compared with certified values. The differences between them showed 0.04% for U-235, 2% for U-234 and 2% for U-236, respectively.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.417-425
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• 2023

The aim of this study was to derive stem taper equations for Quercus acuta, one of main evergreen broad-leaved tree species found in warm temperate regions, and to prepare a stem volume table using those stem taper equations. A total of 688 individual trees were used in the analysis, which were collected from Jeonnam-do, Gyeongnam-do, and Jeju-do. The stem taper models applied to derive the stem curve pattern were the Max and Burkhart, Kozak, and Lee models. Among the three stem taper models, the best explanation of the stem curve shape of Q. acuta was found to be given by the Kozak model, which showed a fitness index of 0.9583, bias of 0.0352, percentage of estimated standard error of 1.1439, and mean absolute deviation of 0.6751. Thus, the stem taper of Q. acuta was estimated using the Kozak model. Moreover,thestemvolumecalculationwasperforme d by applying the Smalian formula to the diameter and height of each stem interval. In addition, an analysis of variance (ANOVA) was conducted to compare the two existing Q. acuta stem volume tables (2007 and 2010) and the newly created stem volume table (2023). This analysis revealed that the stem volume table constructed in the Wando region in 2007 included about twice as much as the stem volume tables constructed in 2010 and 2023. The stem volume table (2023) developed in this study is not only based on the regional collection range and number of utilized trees but also on a sound scientific basis. Therefore, it can be used at the national level as an official stem volume table for Q. acuta.

• Korean Journal of Agricultural Science
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• v.16 no.1
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• pp.84-101
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• 1989

An accurate measurement of size, surface area and volume of agricultural products is essential in many engineering operations such as handling and sorting, and in heat transfer studies on heating and cooling processes. Little information is available on these properties due to their irregular shape, and moreover very little information on the rough rice, soybean, barley, and wheat has been published. Physical dimensions of grain, such as length, width, thickness, surface area, and volume vary according to the variety, environmental conditions, temperature, and moisture content. Especially, recent research has emphasized on the variation of these properties with the important factors such as moisture content. The objectives of this study were to determine physical dimensions such as length, width and thickness, surface area and volume of the rough rice, soybean, barley, and wheat as a function of moisture content, to investigate the effect of moisture content on the properties, and to develop exponential equations to predict the surface area and the volume of the grains as a function of physical dimensions. The varieties of the rough rice used in this study were Akibare, Milyang 15, Seomjin, Samkang, Chilseong, and Yongmun, as a soybean sample Jangyeobkong and Hwangkeumkong, as a barley sample Olbori and Salbori, and as a wheat sample Eunpa and Guru were selected, respectively. The physical properties of the grain samples were determined at four levels of moisture content and ten or fifteen replications were run at each moisture content level and each variety. The results of this study are summarized as follows; 1. In comparison of the surface area and the volume of the 0.0375m diameter-sphere measured in this study with the calculated values by the formula the percent error between them showed least values of 0.65% and 0.77% at the rotational degree interval of 15 degree respectively. 2. The statistical test(t-test) results of the physical properties between the types of rough rice, and between the varieties of soybean and wheat indicated that there were significant difference at the 5% level between them. 3. The physical dimensions varied linearly with the moisture content, and the ratios of length to thickness (L/T) and of width to thickness (W/T) in rough rice decreased with increase of moisture content, while increased in soybean, but uniform tendency of the ratios in barley and wheat was not shown. In all of the sample grains except Olbori, sphericity decreased with increase of moisture content. 4. Over the experimental moisture levels, the surface area and the volume were in the ranges of about $45{\sim}51{\times}10^{-6}m^2$, $25{\sim}30{\times}10^{-9}m^3$ for Japonica-type rough rice, about $42{\sim}47{\times}10^{-6}m^2$, $21{\sim}26{\times}10^{-9}m^3$ for Indica${\times}$Japonica type rough rice, about $188{\sim}200{\times}10^{-6}m^2$, $277{\sim}300{\times}10^{-9}m^3$ for Jangyeobkong, about $180{\sim}201{\times}10^{-6}m^2$, $190{\sim}253{\times}10^{-9}m^3$ for Hwangkeumkong, about $60{\sim}69{\times}10^{-6}m^2$, $36{\sim}45{\times}10^{-9}m^3$ for Covered barley, about $47{\sim}60{\times}10^{-6}m^2$, $22{\sim}28{\times}10^{-9}m^3$ for Naked barley, about $51{\sim}20{\times}10^{-6}m^2$, $23{\sim}31{\times}10^{-9}m^3$ for Eunpamill, and about $57{\sim}69{\times}10^{-6}m^2$, $27{\sim}34{\times}10^{-9}m^3$ for Gurumill, respectively. 5. The increasing rate of surface area and volume with increase of moisture content was higher in soybean than other sample grains, and that of Japonica-type was slightly higher than Indica${\times}$Japonica type in rough rice. 6. The regression equations of physical dimensions, surface area and volume were developed as a function of moisture content, the exponential equations of surface area and volume were also developed as a function of physical dimensions, and the regression equations of surface area were also developed as a function of volume in all grain samples.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.3
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• pp.67-75
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• 2009

Purpose: As to the analytical method of data, the various programs in which it is used for the quantitative rating of the Tl-201 myocardial perfusion SPECT are reported that there is a difference. Therefore, the measured value error of the mutual program is expected to be generated even if the quantitative analysis is made against data of the same patient. Using quantitative index that able to represent myocardial perfusion defect level, we aimed to determine correlation among three myocardial perfusion analysis programs 4DM (4DMSPECT), QPS (Quantitative Perfusion SPECT), ECT (Emory Cardiac Toolbox) that be used generally in most departments of Nuclear Medicine. Materials and Methods: We analyzed the 145 patients who were examined by Tl-201 gated myocardial perfusion SPECT in department of nuclear medicine at Asan Mediacal Center from December 1th 2008 to February 14th 2008. We sorted as normal group and abnormal group. Normal group consist of 80 patients (Male/Female=38/42, age:$65.1{\pm}9.9$) who have low possibility of cardiovascular disease. And abnormal group consist of 65 patients (Male/Female=45/20, age:$63.0{\pm}8.7$) who were diagnosed cardiovascular disease with reversible perfusion defect or fixed perfusion defect through myocardial perfusion SPECT results. Using the 4DM, QPS, and ECT programs, the total defect extent (TDE) such as LAD, LCX, RCA and the summed stress score (SSS) have been analysed for their correlations and statistical comparison with the paried t-test for the quantitative indices analysed from each group. Results: The correlation of 4DM:QPS, QPS:ECT, ECT:4DM each group result from 145 patients is 0.84, 0.86, 0.82 at SSS, 0.87, 0.84, 0.87 at TDE, and both index showed good correlation. In paired t-test and Bland-Altman analysis results showed no statistically significant difference in the comparison of QPS:ECT at the mean SSS and TDE, 4DM:QPS, ECT:4DM comparative analysis results showed statistically significant difference at SSS and TDE index. The correlation of 4DM:QPS, QPS:ECT, ECT:4DM program results from abnormal group (65 patients) is 0.72, 0.72, 0.70 at SSS and 0.77, 0.70, 0.77 at TDE and TDE and SSS has a good correlation. In abnormal group, paired t-test and Bland-Altman analysis results showed no statistically significant difference at QPS:ECT SSS (p=0.89) and TDE (p=0.23) comparison, 4DM:QPS, ECT:4DM comparative analysis results showed statistically significant difference at SSS and TDE index (p<0.01). In normal group (80 patients), paired t-test and Bland-Altman analysis results showed no statistically significant difference at QPS:ECT SSS (p=0.95) and TDE (p=0.73) comparison. And 4DM:QPS, ECT:4DM comparative analysis results showed statistically significant difference at SSS and TDE index (p<0.01). Conclusions: The perfusion defect of the Tl-201 myocardial perfusion SPECT was analyzed in not only the patient in whom it has the cardiovascular disease but also the patient in whom the possibility of the cardiovascular disease is few. In the comparison of the all group research, the mean of the TDE and SSS, 4DM was lower than QPS and ECT progrms. Each program showed good correlation and the results showed statistically significant difference. However, in this way, it is determined to be compatible about the analysis value in which the large-scale side between the programs uses each program a difference in a clinical in the Bland-Altman analyzed result in spite of the good correlation and cannot use. but, this analyzed result will be able to be usefully used as the reference material for the clinical read and is expected.

• Journal of Chest Surgery
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• v.39 no.5 s.262
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• pp.354-358
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• 2006

Background: Pulsatile pumps for extracorporeal circulation have been known to be better for tissue perfusion than non-pulsatile pumps but be detrimental to blood corpuscles. This study is intended to examine the risks and benefits of $T-PLS^{TM}$ through the comparison of clinical effects of $T-PLS^{TM}$ (pulsatile pump) and $Bio-pump^{TM}$ (non-pulsatile pump) used for coronary bypass surgery. Material and Method: The comparison was made on 40 patients who had coronary bypass using $T-PLS^{TM}\;and\;Bio-pump^{TM}$ (20 patients for each) from April 2003 to June 2005. All of the surgeries were operated on pump beating coronary artery bypass graft using cardiopulmonary extra-corporeal circulation. Risk factors before surgery and the condition during surgery and the results were compared. Result: There was no significant difference in age, gender ratio, and risk factors before surgery such as history of diabetes, hypertension, smoking, obstructive pulmonary disease, coronary infarction, and renal failure between the two groups. Surgery duration, hours of heart-lung machine operation, used shunt and grafted coronary branch were little different between the two groups. The two groups had a similar level of systolic arterial pressure, diastolic arterial pressure and mean arterial pressure, but pulse pressure was measured higher in the group with $T-PLS^{TM}\;(46{\pm}15\;mmHg\;in\;T-PLS^{TM}\;vs\;35{\pm}13\;mmHg\;in\;Bio-pump^{TM},\;p<0.05)$. The $T-PLS^{TM}$-operated patients tended to produce more urine volume during surgery, but the difference was not statistically significant $(9.7{\pm}3.9\;cc/min\;in\;T-PLS^{TM}\;vs\;8.9{\pm}3.6\;cc/min\;in\;Bio-pump^{TM},\;p=0.20)$. There was no significant difference in mean duration of respirator usage and 24-hour blood loss after surgery between the two groups. Plasma free Hb was measured lower in the group with $T-PLS^{TM}\;(24.5{\pm}21.7\;mg/dL\;in\;T-PLS^{TM}\;versus\;46.8{\pm}23.0mg/dL\;in\;Bio-pump^{TM},\;p<0.05)$. There was no significant difference in coronary infarction, arrhythmia, renal failure and morbidity rate of cerebrovascular disease. There was a case of death after surgery (death rate of 5%) in the group tested with $T-PLS^{TM}$, but the death rate was not statistically significant. Conclusion: Coronary bypass was operated with $T-PLS^{TM}$ (Pulsatile flow pump) using a heart-lung machine. There was no unexpected event caused by mechanical error during surgery, and the clinical process of the surgery was the same as the surgery for which $Bio-pump^{TM}$ was used. In addition, $T-PLS^{TM}$ used surgery was found to be less detrimental to blood corpuscles than the pulsatile flow has been known to be. Authors of this study could confirm the safety of $T-PLS^{TM}$.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.195-211
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• 2014

Cloud computing services provide IT resources as services on demand. This is considered a key concept, which will lead a shift from an ownership-based paradigm to a new pay-for-use paradigm, which can reduce the fixed cost for IT resources, and improve flexibility and scalability. As IT services, cloud services have evolved from early similar computing concepts such as network computing, utility computing, server-based computing, and grid computing. So research into cloud computing is highly related to and combined with various relevant computing research areas. To seek promising research issues and topics in cloud computing, it is necessary to understand the research trends in cloud computing more comprehensively. In this study, we collect bibliographic information and citation information for cloud computing related research papers published in major international journals from 1994 to 2012, and analyzes macroscopic trends and network changes to citation relationships among papers and the co-occurrence relationships of key words by utilizing social network analysis measures. Through the analysis, we can identify the relationships and connections among research topics in cloud computing related areas, and highlight new potential research topics. In addition, we visualize dynamic changes of research topics relating to cloud computing using a proposed cloud computing "research trend map." A research trend map visualizes positions of research topics in two-dimensional space. Frequencies of key words (X-axis) and the rates of increase in the degree centrality of key words (Y-axis) are used as the two dimensions of the research trend map. Based on the values of the two dimensions, the two dimensional space of a research map is divided into four areas: maturation, growth, promising, and decline. An area with high keyword frequency, but low rates of increase of degree centrality is defined as a mature technology area; the area where both keyword frequency and the increase rate of degree centrality are high is defined as a growth technology area; the area where the keyword frequency is low, but the rate of increase in the degree centrality is high is defined as a promising technology area; and the area where both keyword frequency and the rate of degree centrality are low is defined as a declining technology area. Based on this method, cloud computing research trend maps make it possible to easily grasp the main research trends in cloud computing, and to explain the evolution of research topics. According to the results of an analysis of citation relationships, research papers on security, distributed processing, and optical networking for cloud computing are on the top based on the page-rank measure. From the analysis of key words in research papers, cloud computing and grid computing showed high centrality in 2009, and key words dealing with main elemental technologies such as data outsourcing, error detection methods, and infrastructure construction showed high centrality in 2010~2011. In 2012, security, virtualization, and resource management showed high centrality. Moreover, it was found that the interest in the technical issues of cloud computing increases gradually. From annual cloud computing research trend maps, it was verified that security is located in the promising area, virtualization has moved from the promising area to the growth area, and grid computing and distributed system has moved to the declining area. The study results indicate that distributed systems and grid computing received a lot of attention as similar computing paradigms in the early stage of cloud computing research. The early stage of cloud computing was a period focused on understanding and investigating cloud computing as an emergent technology, linking to relevant established computing concepts. After the early stage, security and virtualization technologies became main issues in cloud computing, which is reflected in the movement of security and virtualization technologies from the promising area to the growth area in the cloud computing research trend maps. Moreover, this study revealed that current research in cloud computing has rapidly transferred from a focus on technical issues to for a focus on application issues, such as SLAs (Service Level Agreements).

• 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.