• 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.

• Journal of Korean Society of Forest Science
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• v.101 no.1
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• pp.36-45
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• 2012

Education on the environment and plants offered by arboretum for today's people not only contribute to foster a better natural environment in urban region but also provide visitors with decent refreshment environment and beyond. In the study, the author undertook the observation on usage behavior and satisfaction model of arboretum visitors expect and investigated the facilities and programs to be offered by arboretum in order to propose the opinion regarding the service. For observation size of variables in a multiple regression analysis of variables is influencing satisfaction rankings walks the line of flow, the educational effect on the environment, cleanliness of the facility, visits pay, natural beauty, diversity of trees, accessibility and friendliness of staff, expansion of facilities in the arboretum and appeared as a complement. In case of visitor attribute, the residents living near the facility showed the highest visit frequency of more than 5 times, especially as part of taking a walk. This proves that the visit to arboretum is considered as part of everyday life, and thus a new program and walk path as well as movement route are needed to be developed for the visitors. In the question relating to the facilities and operation programs in Daegu Arboretum, particularly the requests by visitors, they responded that the establishment of cultural event, beautiful natural scenery, refreshment and convenience facilities is the most critical issue. In addition, the management on withered trees and bare lands is an urgent issue as well. In this sense, the Operation and Management Strategies based upon the visitor behaviors and model of satisfaction are needed to deal with the adoption of diverse events and festivals joined by local residents, ombudsman program, environmental program development for students and teachers within the region, negligent bare lands and withered tree replacement, and cafeteria facility improvement and supplement as well as the bench marking of other facilities than arboretums located in other regions. These items are thought to be sufficiently dealt with by Daegu Arboretum having no more external resources. It is recognized that the visitor satisfaction begins from a minor thing, and a small difference determines a great satisfaction, and thus the software approach rather than hardware one is in need.

• Korean Journal of Environmental Agriculture
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• v.18 no.1
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• pp.77-82
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• 1999

Three floating islands have been constructed for water quality improvement for a polluted irrigation reservoir. Each floating island consists of 10 segments. Each segment hay an area of $16m^2$(4×4m) and is made of wood frames and floats(polystyrene foam). We planted three species of aquatic macrophytes(Typha angustifolia, Zizania latifolia, and Phragmites australis) in floating island on June, 1998. They grew very well without death. We would like to evaluate Phragmites australis is the most suitable aquatic macrophyte that could be planted in a floating island because it maintained the best balance of its root and shoot among them. During their grown period, net primary productivity of Typha angustifolia was $962gDM/m^2$, Zizania latifolia was $1,115gDM/m^2$, and Phragmites australis was $523gDM/m^2$. From these data, it would be estimated to 5.0Kg uptake of nitrogen by aquatic macrophytes and phosphorus 0.8Kg in 3 floating islands. The floating islands worked well as a habitat of fish and prawns. Many kinds of insect lived on the floating islands. The floating island has not only the function of water quality treatment but also several advantages: improvement of landscape and species diversity; low cost of maintenance; low technology; unnecessary of energy; less susceptible to variations in pollutant loading. It could be evaluated a good measure of water quality improvement for an irrigation reservoir. However, it should be intensively studied to develop more light, strong, durable and low-priced frames for efficient floating islands.

• Management & Information Systems Review
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• v.36 no.4
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• pp.33-51
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• 2017

With the advent of 'The Forth Industrial Revolution' and the growing demand for quality of life due to economic growth, needs for the quality of medical services are increasing. Artificial intelligence has been introduced in the medical field, but it is rarely used in chronic skin diseases that directly affect the quality of life. Also, atopic dermatitis, a representative disease among chronic skin diseases, has a disadvantage in that it is difficult to make an objective diagnosis of the severity of lesions. The aim of this study is to establish an intelligent severity recognition model of atopic dermatitis for improving the quality of patient's life. For this, the following steps were performed. First, image data of patients with atopic dermatitis were collected from the Catholic University of Korea Seoul Saint Mary's Hospital. Refinement and labeling were performed on the collected image data to obtain training and verification data that suitable for the objective intelligent atopic dermatitis severity recognition model. Second, learning and verification of various CNN algorithms are performed to select an image recognition algorithm that suitable for the objective intelligent atopic dermatitis severity recognition model. Experimental results showed that 'ResNet V1 101' and 'ResNet V2 50' were measured the highest performance with Erythema and Excoriation over 90% accuracy, and 'VGG-NET' was measured 89% accuracy lower than the two lesions due to lack of training data. The proposed methodology demonstrates that the image recognition algorithm has high performance not only in the field of object recognition but also in the medical field requiring expert knowledge. In addition, this study is expected to be highly applicable in the field of atopic dermatitis due to it uses image data of actual atopic dermatitis patients.

• Korean Chemical Engineering Research
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• v.56 no.1
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• pp.6-13
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• 2018

Utilizing the heat of cooling water discharge of coal-fired power plant, pipeline investment costs for businesses that supply heat to agricultural facilities near power plants increase in proportion to installation distance. On one hand, the distance from the power plant is a factor that brings difficulties to secure economic efficiency. On the other, if the installation distance is short, there is a problem of securing the heating demands, facility houses, which causes economical efficiency to suffer. In this study, the economic efficiency of 1km length of standard heat pipeline was evaluated. The sensitivity of the heat pipe to the new length variation was analyzed at the level of government subsidy, amount of heating demand and the incremental rate of pipeline with additional government subsidy. As a result of the analysis, it was estimated that NPV 131 million won and IRR 15.73%. The sensitivity analysis showed that NPV was negative when the length of heat pipe facility exceeded 2.6 km. If the government supports 50% of the initial investment, the efficiency is secured within the estimated length of 5.3 km, and if it supports 80%, the length increases within 11.4 km. If the heat demand is reduced to less than 62% at the new length of the standard heat pipe, it is expected economic efficiency is not obtained. If the ratio of government subsidies to initial investment increases, the elasticity of the new bloc will increase, and the fixed investment, which is the cost of capital investment for one unit of heating demand, will decrease. This would result in a reduction in the cost of production per unit, and it would be possible to supply heat at a cheaper price level to the facility farming. Government subsidies will result in the increased economic availability of hot plumbing facilities and additional efficiencies due to increased demand. The greater government subsidies to initial investment, the less farms cost due to the decrease in the price per unit. The results of the study are significant in terms of the economic evaluation of the effectiveness of the government subsidy for the thermal power plant heat utilization project. The implication can be applied to any related pilot to come.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.175-191
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• 2011

With the MICE(Meeting, Incentive travel, Convention, Exhibition) industry coming into the spotlight, there has been a growing interest in the domestic exhibition industry. Accordingly, in Korea, various studies of the industry are being conducted to enhance exhibition performance as in the United States or Europe. Some studies are focusing particularly on analyzing visiting patterns of exhibition visitors using intelligent information technology in consideration of the variations in effects of watching exhibitions according to the exhibitory environment or technique, thereby understanding visitors and, furthermore, drawing the correlations between exhibiting businesses and improving exhibition performance. However, previous studies related to booth recommendation systems only discussed the accuracy of recommendation in the aspect of a system rather than determining changes in visitors' behavior or perception by recommendation. A booth recommendation system enables visitors to visit unplanned exhibition booths by recommending visitors suitable ones based on information about visitors' visits. Meanwhile, some visitors may be satisfied with their unplanned visits, while others may consider the recommending process to be cumbersome or obstructive to their free observation. In the latter case, the exhibition is likely to produce worse results compared to when visitors are allowed to freely observe the exhibition. Thus, in order to apply a booth recommendation system to exhibition halls, the factors affecting the performance of the system should be generally examined, and the effects of the system on visitors' unplanned visiting behavior should be carefully studied. As such, this study aims to determine the factors that affect the performance of a booth recommendation system by reviewing theories and literature and to examine the effects of visitors' perceived performance of the system on their satisfaction of unplanned behavior and intention to reuse the system. Toward this end, the unplanned behavior theory was adopted as the theoretical framework. Unplanned behavior can be defined as "behavior that is done by consumers without any prearranged plan". Thus far, consumers' unplanned behavior has been studied in various fields. The field of marketing, in particular, has focused on unplanned purchasing among various types of unplanned behavior, which has been often confused with impulsive purchasing. Nevertheless, the two are different from each other; while impulsive purchasing means strong, continuous urges to purchase things, unplanned purchasing is behavior with purchasing decisions that are made inside a store, not before going into one. In other words, all impulsive purchases are unplanned, but not all unplanned purchases are impulsive. Then why do consumers engage in unplanned behavior? Regarding this question, many scholars have made many suggestions, but there has been a consensus that it is because consumers have enough flexibility to change their plans in the middle instead of developing plans thoroughly. In other words, if unplanned behavior costs much, it will be difficult for consumers to change their prearranged plans. In the case of the exhibition hall examined in this study, visitors learn the programs of the hall and plan which booth to visit in advance. This is because it is practically impossible for visitors to visit all of the various booths that an exhibition operates due to their limited time. Therefore, if the booth recommendation system proposed in this study recommends visitors booths that they may like, they can change their plans and visit the recommended booths. Such visiting behavior can be regarded similarly to consumers' visit to a store or tourists' unplanned behavior in a tourist spot and can be understand in the same context as the recent increase in tourism consumers' unplanned behavior influenced by information devices. Thus, the following research model was established. This research model uses visitors' perceived performance of a booth recommendation system as the parameter, and the factors affecting the performance include trust in the system, exhibition visitors' knowledge levels, expected personalization of the system, and the system's threat to freedom. In addition, the causal relation between visitors' satisfaction of their perceived performance of the system and unplanned behavior and their intention to reuse the system was determined. While doing so, trust in the booth recommendation system consisted of 2nd order factors such as competence, benevolence, and integrity, while the other factors consisted of 1st order factors. In order to verify this model, a booth recommendation system was developed to be tested in 2011 DMC Culture Open, and 101 visitors were empirically studied and analyzed. The results are as follows. First, visitors' trust was the most important factor in the booth recommendation system, and the visitors who used the system perceived its performance as a success based on their trust. Second, visitors' knowledge levels also had significant effects on the performance of the system, which indicates that the performance of a recommendation system requires an advance understanding. In other words, visitors with higher levels of understanding of the exhibition hall learned better the usefulness of the booth recommendation system. Third, expected personalization did not have significant effects, which is a different result from previous studies' results. This is presumably because the booth recommendation system used in this study did not provide enough personalized services. Fourth, the recommendation information provided by the booth recommendation system was not considered to threaten or restrict one's freedom, which means it is valuable in terms of usefulness. Lastly, high performance of the booth recommendation system led to visitors' high satisfaction levels of unplanned behavior and intention to reuse the system. To sum up, in order to analyze the effects of a booth recommendation system on visitors' unplanned visits to a booth, empirical data were examined based on the unplanned behavior theory and, accordingly, useful suggestions for the establishment and design of future booth recommendation systems were made. In the future, further examination should be conducted through elaborate survey questions and survey objects.