• 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.

• (The) Research of the performance art and culture
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• no.32
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• pp.701-738
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• 2016

The Geomungo(거문고) is a instrument of Gogureoy(高句麗). The instrument had covered a lot of Korea, so it have become a important musical instrument in Korea. Hayasi Genjo(林謙三), Japanese scholar, had maintained his opinion that the Geomungo of Gogureoy is the Wagonghu(臥??), and the Geomungo was formed later, the record of Kimbusik, wrighter of the History of Three Kingdom(三國史記), was incredible. Lee-Hyegu refuted his hypothesis because the introduction on the Wagonghu of Japan have been inaccurate. Since then, many scholars of Korea have studed on the Geomungo of Gogureoy. But their study of the Geomungo was inclined to the topic, relation of the Geomungo and the Wagonghu, or the Wagonghu, the origin of the Geomungo. And They have thought that the record of Kimbusik's was truth. Kimbusik had recorded that Wangsanak(王山岳) had made the Geomungo from the Chilheoyn-Geum(七絃琴, Seven stringed Zither. 古琴). But the Geomungo was different from Geum(琴), but similar to Wagonghu. Many ancient tomb have been unearthed in the old land of Gogureoy, and the were many tomb painting of Gogureoy Geomungo. They were many different style, the form, the size, the number of strings and the position of the musician. So I think that many various type of the Geomungo had been exsited in Gogureoy they had become a prestyle of the Geomungo. The Geomungo was originated from the Wagonghu, its form was similar to the Geomungo. The many scholars considered that it is truth, the Wagonghu was handed down from China, and was spreded to Japan. But there were the Wagonghu in the early Joseon(古朝鮮), The song of the early Joseon, Gongmudohaga(公無渡河歌). The song was accompanied by the Wagonghu. We can read off, at the Song, the Wagonghu had exsisted in the early Joseon. So I think cautiously on that point, the Wagonghu of the Early Joseon was old than that of China, and thd Geomungo of Gogureoy was originated from the Wagonghu of the Early Joseon.

• Journal of the Daesoon Academy of Sciences
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• v.29
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• pp.71-100
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• 2017

This study focuses on examining 'the Supreme God and Celestial Worthy of the Ninth Heaven Who Spreads the Sound of the Thunder Corresponding to Primordial Origin', which Daesoon Jinrihoe believes in as the highest divinity. The name of this divinity was first found in Chinese Daoist scriptures. This study starts by considering the global propagation of virtue and then research connected to this topic. There are two alternative names for this divinity in relation to his human avatar, Kang Jeungsan, the subject of faith in Daesoon Jinrihoe. One is 'the Lord God of Great Creation in the Ninth Heaven' meaning the divinity before assuming a human avatar, and the other is 'the Celestial Worthy of Universal Transformation' the same divinity after he discarded his human avatar and returned to his celestial post. To understand how the belief system of Daesoon Jinrihoe differs from that of Daoism, it is necessary to study the divinity's change from being 'the Lord God of Great Creation in the Ninth Heaven' to becoming 'the Celestial Worthy of Universal Transformation'. If this distinction is not made clear, it brings about confusing arguments concerning the term 'Supreme God (Sangje)' as used in Daoism and Daesoon Jinrihoe. In order to offer a specific explanation, this study suggests three possible directions. The first hypothesis is that although these two names, 'the Celestial Worthy of the Ninth Heaven Who Spreads the Sound of the Thunder Corresponding to Primordial Origin' from Daoism and 'the Supreme God of the Ninth Heaven Who Spreads the Sound of the Thunder Corresponding to Primordial Origin' from Daesoon Jinrihoe, are similar, they actually have nothing to do with one another. The second hypothesis is that they are in fact the same divinity. Lastly, the third hypothesis is that they are closely connected, however, the former (the Celestial Worthy of the Ninth Heaven Who Spreads the Sound of the Thunder Corresponding to Primordial Origin) is a position needed to fulfill the mission of Jeungsan, whereas the latter (the Supreme God of the Ninth Heaven Who Spreads the Sound of the Thunder Corresponding to Primordial Origin) is a name received after the human avatar passes and the deity returns to the Noebu, 'the department of lightning'. These hypotheses face certain problems such as arbitrary mixing, the need for the theoretical clarity, and argumental weakness. Therefore, by leaving some unresolved questions, this study encourages future follow-up studies.