• Toxicological Research
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• 제17권
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• pp.37-39
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• 2001

As the preliminary data collection for further chemical risk assessment. toxicants rating works is now rather extensively implemented in China. It consists of two parts, ie., rating of the hazard level of the exposed toxicant and that of the toxicant's profession. In the first part, the rating are based on six criteria, ie., acute toxicity, incidence of acute poisoning, prevalence of chronic poisoning, consequence of chronic poisoning, carcinogenecity and MAC level. Four hazardous levels are to be classified as extreme, high, medium, mild. In the second part. three determinants as weighted coefficients are taken into account, ie., toxicant's hazard level. exposure time and folds of MAC surpassing. Eventually, the index of classification C by which the work with toxic hazard can be classified is able to be calculated and assessed. Several comments were discussed and new recommendations were demonstrated.

• 한국콘텐츠학회논문지
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• 제14권12호
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• pp.598-606
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• 2014

미국과 일본이 민간자율의 등급분류제도를 운영하고 있는 것과 달리 프랑스와 우리나라는 공공기관이 이 기능을 담당하고 있다. 하지만 제한상영관이 없는 제한상영가 등급의 문제점, 방송을 비롯한 매체들은 영화와 달리 사전심의를 받지 않는다는 점, 비용부담의 문제 등을 감안할 때 우리의 제도는 개선이 필요하고, 대안 중 하나가 등급분류기관을 민간자율로 전환하는 것이다. 민간자율의 등급분류제도 도입방법은 메이저 영화사들이 중심이 되어 등급분류협회를 설립하고 그 산하에 등급분류위원회를 두되 위원회 운영의 독립성을 보장하는 것이다. 정부는 저예산 예술영화의 심의료를 지원하고 청소년보호단체 등이 공정한 심의과정과 결과 준수를 감시하도록 지원할 필요가 있다. 영화산업이 등급분류제도의 민간자율화에 적극 나서지 않는 다는 사실이 가장 큰 걸림돌이지만, 표현의 자유 신장을 위해 합리적 방안을 찾는 노력이 필요하다.

• Asia pacific journal of information systems
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• 제21권2호
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• pp.43-58
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• 2011

Support vector machines (SVMs), a machine learning technique, has been applied to not only binary classification problems such as bankruptcy prediction but also multi-class problems such as corporate credit ratings. However, in general, the performance of SVMs can be easily worse than the best alternative model to SVMs according to the selection of predictors, even though SVMs has the distinguishing feature of successfully classifying and predicting in a lot of dichotomous or multi-class problems. For overcoming the weakness of SVMs, this study has proposed an approach for selecting features for multi-class SVMs that utilize the impurity measures of classification trees. For the selection of the input features, we employed the C4.5 and CART algorithms, including the stepwise method of discriminant analysis, which is a well-known method for selecting features. We have built a multi-class SVMs model for credit rating using the above method and presented experimental results with data regarding S&P 500 companies.

• 한국BIM학회 논문집
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• 제6권2호
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• pp.29-38
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• 2016

Road bridges are deteriorating gradually, and it is forecasted that the number of road bridges aging over 30 years will increase by more than 3 times of the current number. To maintain road bridges in a safe condition, current safety conditions of the bridges must be estimated for repair or reinforcement. However, budget and professional manpower required to perform in-depth inspections of road bridges are limited. This study proposes an estimation model for safety rating of road bridges by analyzing the data from Facility Management System (FMS) and Yearbook of Road Bridges and Tunnel. These data include basic specifications, year of completion, traffic, safety rating, and others. The distribution of safety rating was imbalanced, indicating 91% of road bridges have safety ratings of A or B. To improve classification performance, five safety ratings were integrated into two classes of G (good, A and B) and P (poor ratings under C). This rearrangement was set because facilities with ratings under C are required to be repaired or reinforced to recover their original functionality. 70% of the original data were used as training data, while the other 30% were used for validation. Data of class P in the training data were oversampled by 3 times, and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm was used to develop the estimation model. The results of estimation model showed overall accuracy of 84.8%, true positive rate of 67.3%, and 29 classification rule. Year of completion was identified as the most critical factor on affecting lower safety ratings of bridges.

• Asia pacific journal of information systems
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• 제25권3호
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• pp.579-596
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• 2015

Many studies have focused on the prediction of corporate credit rating using various data mining techniques. One of the most frequently used algorithms is support vector machines (SVM), and recently, novel techniques such as SVM+ and SVM+MTL have emerged. This paper intends to show the applicability of such new techniques to multi-classification and corporate credit rating and compare them with conventional SVM regarding prediction performance. We solve multi-class SVM+ and SVM+MTL problems by constructing several binary classifiers. Furthermore, to demonstrate the robustness and outstanding performance of SVM+MTL algorithm over other techniques, we utilized four typical multi-class processing methods in our experiments. The results show that SVM+MTL outperforms both conventional SVM and novel SVM+ in predicting corporate credit rating. This study contributes to the literature by showing the applicability of new techniques such as SVM+ and SVM+MTL and the outperformance of SVM+MTL over conventional techniques. Thus, this study enriches solving techniques for addressing multi-class problems such as corporate credit rating prediction.

• 대한안전경영과학회지
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• 제10권2호
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• pp.71-83
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• 2008

Most researches on the corporate credit rating are generally classified into the area of bankruptcy prediction and bond rating. The studies on bankruptcy prediction have focused on improving the performance in binary classification problem, since the criterion variable is categorical, bankrupt or non-bankrupt. The other studies on bond rating have predicted the credit ratings, which was already evaluated by bond rating experts. The financial institute, however, should perform effective loan evaluation and risk management by employing the corporate credit rating model, which is able to determine the credit of corporations. Therefore, in this study we present a medium sized corporate credit rating system by using Artificial Neural Network(ANN) and Analytical Hierarchy Process(AHP). Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the ANN and AHP model using both financial information and non-financial information. Finally, the credit ratings of each firm are assigned by the proposed method.

• 디지털융복합연구
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• 제17권12호
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• pp.91-108
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• 2019

게임은 세계인의 보편적인 여가문화로 자리 잡게 되었다. 그러나 게임 산업 발전에 따른 여러 긍정적인 영향과 함께 청소년들에게 악영향을 미치거나, 범죄 행위를 조장하는 등의 다양한 사회적 문제 또한 초래하고 있다. 이러한 영향을 최소화하기 위해서 세계 각 국가에서는 아동·청소년부터 일반 대중에게까지 연령과 사회적 가치에 적절한 게임물을 제공할 수 있도록 게임물 등급분류제도를 운용하고 있다. 게임이라는 새로운 디지털 콘텐츠에 대한 제도인 만큼 아직 등급분류 제도의 한계점은 여전히 많은 연구들에서 논의되고 있다. 이에 본 연구에서는 국내를 포함하여 다양한 국가의 게임등급분류제도를 조사하여 글로벌 게임규제 현황을 고찰한다. 또한 우리나라의 게임물 등급분류제도와 다른 국가의 등급분류제도를 비교분석하여 우리의 제도가 보다 적절하게 개선될 수 있는 방향을 제시한다. 본 연구 결과는 국내 게임물 등급분류제도의 실효성 확보와 표준화에 기여할 것이다.

• 한국정보시스템학회지:정보시스템연구
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• 제14권1호
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• pp.41-65
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• 2005

The credit ration is a significant area of financial management which is of major interest to practitioners, financial and credit analysts. The components of credit rating are identified decision models are developed to assess credit rating an the corresponding creditworthiness of firms an accurately ad possble. Although many early studies demonstrate a priori which of these techniques will be most effective to solve a specific classification problem. Recently, a number of studies have demonstrate that a hybrid model integration artificial intelligence approaches with other feature selection algorthms can be alternative methodologies for business classification problems. In this article, we propose a hybrid approach using rough set theory as an alternative methodology to select appropriate attributes for case-based reasoning. This model uses rough specific interest lies in lthe stable combining of both rough set theory to extract knowledge that can guide dffective retrevals of useful cases. Our specific interest lies in the stable combining of both rough set theory and case-based reasoning in the problem of corporate credit rating. In addition, we summarize backgrounds of applying integrated model in the field of corporate credit rating with a brief description of various credit rating methodologies.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2003년도 봄 학술발표회 논문집
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• pp.265-272
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• 2003

In this study, the rock mass classification results from the face mapping and the resistivity inversion data are compared and analyzed for the reliability investigation of the determination of the rock support type based on the surface electrical survey. To get the quantitative correlation, rock engineering indices such as RCR(rock condition rating), N(Rock mass number), Q-system based on RMR(rock mass rating) are calculated. Kriging method as a post processing technique for global optimization is used to improve its resolution. The result of correlation analysis shows that the geological condition estimated from 2D electrical resistivity survey is coincident globally with the trend of rock type except for a few local areas. The correlation between the results of 3D electrical resistivity survey and the rock mass classification turns out to be very high. It can be concluded that 3D electrical resistivity survey is powerful to set up the reliable rock support type.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권4호
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• pp.331-334
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• 2006

Original Support Vsctor Machines (SVMs) by Vapnik were used for binary classification problems. Some researchers have tried to extend original SVM to multiclass classification. However, their studies have only focused on classifying samples into nominal categories. This study proposes a novel multiclass SVM model in order to handle ordinal multiple classes. Our suggested model may use less classifiers but predict more accurately because it utilizes additional hidden information, the order of the classes. To validate our model, we apply it to the real-world bond rating case. In this study, we compare the results of the model to those of statistical and typical machine learning techniques, and another multi class SVM algorithm. The result shows that proposed model may improve classification performance in comparison to other typical multiclass classification algorithms.