• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.79-95
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• 2012

As credit loan products significantly increase in most financial institutions, the number of fraudulent transactions is also growing rapidly. Therefore, to manage the financial risks successfully, the financial institutions should reinforce the qualifications for a loan and augment the ability to detect a credit loan fraud proactively. In the process of building a classification model to detect credit loan frauds, utility from classification results (i.e., benefits from correct prediction and costs from incorrect prediction) is more important than the accuracy rate of classification. The objective of this paper is to propose a new approach to building a classification model for detecting credit loan fraud based on an individual-level utility. Experimental results show that the model comes up with higher utility than the fraud detection models which do not take into account the individual-level utility concept. Also, it is shown that the individual-level utility computed by the model is more accurate than the mean-level utility computed by other models, in both opportunity utility and cash flow perspectives. We provide diverse views on the experimental results from both perspectives.

• Journal of Intelligence and Information Systems
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• v.14 no.3
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• pp.133-154
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• 2008

The data imbalance problem which can be uncounted in data mining classification problems typically means that there are more or less instances in a class than those in other classes. In order to solve the data imbalance problem, there has been proposed a number of techniques based on re-sampling with replacement, adjusting decision thresholds, and adjusting the cost of the different classes. In this paper, we study the feasibility of the combination usage of the techniques previously proposed to deal with the data imbalance problem, and suggest a combination method using genetic algorithm to find the optimal combination ratio of the techniques. To improve the prediction accuracy of a minority class, we determine the combination ratio based on the F-value of the minority class as the fitness function of genetic algorithm. To compare the performance with those of single techniques and the matrix-style combination of random percentage, we performed experiments using four public datasets which has been generally used to compare the performance of methods for the data imbalance problem. From the results of experiments, we can find the usefulness of the proposed method.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.12
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• pp.1259-1263
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• 2010

SaaS is an emerging paradigm for software development and deployment, expected to able to reduce cost. SaaS is also considered as a crucial technology for implementation of cutting-edge technology, such as distributed computing, green computing, and cloud computing. SaaS is requested to be configurable software to satisfy various customers, therefore it is very important to consider every configurability requirement during requirement elicitation. Our research suggests a classification technique to secure completeness of configuration requirement.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.25-41
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• 2011

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved them more powerful than traditional artificial neural networks (ANNs) (Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al., 2005; Kim, 2003).The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is so cost-sensitive particularly in financial classification problems such as the credit ratings that if the credit ratings are misclassified, a terrible economic loss for investors or financial decision makers may happen. Therefore, it is necessary to convert the outputs of the classifier into wellcalibrated posterior probabilities-based multiclass credit ratings according to the bankruptcy probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create the probabilities (Platt, 1999; Drish, 2001). This paper applied AdaBoost algorithm-based support vector machines (SVMs) into a bankruptcy prediction as a binary classification problem for the IT companies in Korea and then performed the multi-class credit ratings of the companies by making a normal distribution shape of posterior bankruptcy probabilities from the loss functions extracted from the SVMs. Our proposed approach also showed that their methods can minimize the misclassification problems by adjusting the credit grade interval ranges on condition that each credit grade for credit loan borrowers has its own credit risk, i.e. bankruptcy probability.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.1
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• pp.27-35
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• 2022

In this paper we propose white noise adding method to prevent missclassification of deep learning system by adversarial attacks. The proposed method is that adding white noise to input image that is benign or adversarial example. The experimental results are showing that the proposed method is robustness to 3 adversarial attacks such as FGSM attack, BIN attack and CW attack. The recognition accuracies of Resnet model with 18, 34, 50 and 101 layers are enhanced when white noise is added to test data set while it does not affect to classification of benign test dataset. The proposed model is applicable to defense to adversarial attacks and replace to time- consuming and high expensive defense method against adversarial attacks such as adversarial training method and deep learning replacing method.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.593-601
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• 2022

We call "fruad" the cases that are not frequently occurring but cause significant losses. Fraud detection is commonly encountered in various applications, including wafer production in the semiconductor industry. It is not trivial to directly extend the standard binary classification methods to the fraud detection context because the misclassification cost is much higher than the normal class. In this article, we propose the functional fraud detection support vector machine (F²DSVM) that extends the fraud detection support vector machine (FDSVM) to handle functional covariates. The proposed method seeks a classifier for a function predictor that achieves optimal performance while achieving the desired sensitivity level. F²DSVM, like the conventional SVM, has piece-wise linear solution paths, allowing us to develop an efficient algorithm to recover entire solution paths, resulting in significantly improved computational efficiency. Finally, we apply the proposed F²DSVM to the defective wafer detection problem and assess its potential applicability.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.155-162
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• 2001

Recently, in Korea, a few construction companies have been tried to develop a management system, which is able to integrate schedule and cost. In spite of these attempts, however, advanced management techniques can be hardly applied under the BoQ based management system. In order to improve these problems, many studies have been peformed, but yet could not overcome practical limitations. Besides, the application of historical data is below the level since it is so difficult to accumulate and feed-back historical data under the unique character of construction industry. Consequently, lots of time and effort have being wasted to establish control criteria. The newly generated Information is not systematically managed as well. Therefore, this study suggests Integrated Construction Management System complemented the existing practical problems.

• Magazine of the Korean Society of Agricultural Engineers
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• v.40 no.2
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• pp.18-28
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• 1998

현재의 가축분뇨처리 형태와 축종별 적용대상은 표 3과 같이 분류되며, 축분 퇴비화 뇨오수 정화방식의 고액분리 형태에서 분뇨혼합물의 동시처리 즉, 깔감축사(소, 돼지), 슬러리 발효 퇴비화를 통한 무방류 시스템으로 변화되는 추세이다. 이러한 현상은 최근의 방류수질 규제강화에 따른 무방류화 저비용 단순화를 지향하고 있으며, 무방류화의 가장 큰 걸림돌은 톱밥과 같은 수분조절재 부족이라 할 수 있다. 현재 이용되고 있는 주요 시스템별 개선안은 다음과 같다. $\square$ 주요 처리 시스템 적용의 문제제기 $\circ$ 축분발효시설 - 톱밥 등 부자재 무이용을 위한 축분의 예비 건조시설 이용 - 교반식 발효기의 악취확산방지를 위하여 밀폐형 하우스 및 강제환기 장치 설치에 의한 악취 포집 및 탈취 - 중소규모 발효시설 개발 $\circ$ 화력건조 - 예비건조를 통한 톱밥, 연료비용 절감 - 악취방치 장치부착 의무화 $\circ$ 활성오니 정화시설 - 시설 운전관리 단순화 - 1차 고액분리를 통한 유입수 오염부하량 감소 및 균일화 $\circ$ 깔감축사 - 경제적으로 흡수를 극대화할 수 있는 수분조절재의 조합비 결정 및 혼합물을 깔감으로 재이용 기술개발 $\circ$ 슬러리 발효퇴비화 시설 - 톱밥 등 수분조절재 사용량 절감기술 - 증발량 극대화 기술개발 및 소요에너지 최소화기술개발 우리나라는 사계절이 뚜렷하여 외기상에 따라 편차가 심하며, 고밀도 사육 및 지역적 편중성, 경지면적 협소, 고비용 시장구조 등의 축산환경을 고려한다면 근원적으로 가축분뇨문제 해결한다는 것은 결코 쉬운 일이 아니다. 이는 저공해 사료개발에서부터 분뇨가 발생단계에서 분리, 수거할 수 있는 수거시설, 고효율 분뇨처리시스템의 정립, 액비 및 퇴비의 가공, 토양환원되었을 때 작물생장장애, 액비, 퇴비의 유통, 가축분뇨처리 정책 및 규제법 등의 각 분야에서 복합적 노력이 필요하다.

• 한국체육학회지인문사회과학편
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• v.55 no.4
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• pp.285-292
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• 2016

There are efforts to optimize costs and maximize effectiveness of sports events with changing cognition. Proper organizational structure can be initiative to do these, so there are necessities of propose functional areas at the management perspective. With these background, the purpose of this study is to classify functional areas of Olympic at the management aspect. For this purpose, this study conduct four steps delphi research with total 7 experts. First step, experts conduct categorizing and itemizing based on 69 functional areas of 2016 Rio Olympic. Second step, experts provide opinion about first step results and third step, experts propose their point of view regarding to core issues. Fourth step, experts conduct group discussion for drawing final results.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.45-63
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• 2019

Until recently, most people have invested in a traditional portfolio consisting of stocks, bonds and real estates based on the three-division method of properties in Korea. However, this study analyzed the impact of the composition of a portfolio combining representative real estate indirect investment products such as Reits and real estate funds on the investment performance. For this purpose, the empirical analysis using the mean variance model, which is the most appropriate method for the portfolio composition, was used. For variables used in this study, mixed asset portfolios were classified into Portfolio A through Portfolio G depending on the composition of assets, and the price indices selected as Kospi, Krx bond, Reits Trus Y7, Hanwha-Lasal fund, and Office (Seoul). The results are as follows; first Portfolio D, which combined bonds, stocks, Reits and Real Estate funds, and Portfolio G, which added the office, the actual real estate, were shown to have the lowest risk. second, Portfolio B composed of bonds, stocks and Reits and Portfolio D with added real estate funds had the lowest risk while Portfolio F composed of bonds, stocks, offices and real estate funds, and Portfolio G with added Reits were the most profitable. As a result, it has been analyzed that it was more effective to compose a portfolio including Reits and real estate funds, which were real estate indirect investment products that eliminated the illiquidity limitation of real estates than real estates, the traditional three-division method of properties. Therefore, it is possible to minimize the risk of investors and reduce the cost of ownership of the real estate by solving the illiquidity problem that is the biggest disadvantage of the direct investment, In addition, it is considered that it is more necessary to reinvigorate the real estate indirect investment market where small amounts can be invested.