• 한국경영정보학회:학술대회논문집
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• 한국경영정보학회 2007년도 International Conference
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• pp.608-613
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• 2007

Prediction of corporate bankruptcies has long been an important topic and has been studied extensively in the finance and management literature because it is an essential basis for the risk management of financial institutions. Recently, support vector machines (SVMs) are becoming popular as a tool for bankruptcy prediction because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. In addition, they don't require huge training samples and have little possibility of overfitting. However. in order to Use SVM, a user should determine several factors such as the parameters ofa kernel function, appropriate feature subset, and proper instance subset by heuristics, which hinders accurate prediction results when using SVM In this study, we propose a novel hybrid SVM classifier with simultaneous optimization of feature subsets, instance subsets, and kernel parameters. This study introduces genetic algorithms (GAs) to optimize the feature selection, instance selection, and kernel parameters simultaneously. Our study applies the proposed model to the real-world case for bankruptcy prediction. Experimental results show that the prediction accuracy of conventional SVM may be improved significantly by using our model.

• 지능정보연구
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• 제7권2호
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• pp.83-93
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• 2001

기업부실예측은 과거로부터 많은 연구가 이루어진 분야로, 주로 통계기법에 의한 분류예측문제로 다루어져 왔다. 최근에는 인공신경망, 의사결정나무 등 비선형성을 반영할 수 있는 인공지능 기법을 적용한 연구가 많이 수행되고 있다. 본 연구에서는 최적화에 주로 활용하는 인공지능 기법인 유전자 알고리즘을 규칙추출을 통한 기업부실예측 모형의 개발에 적용하고, 활용가능성을 검증하였다.

• 디지털융복합연구
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• 제13권3호
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• pp.155-165
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• 2015

기업부도예측은 재무 분야에 있어 중요한 연구주제 중 하나로 1960년대 이후부터 꾸준히 연구되어져 왔다. 국내의 경우, IMF 사태 이후 기업부도예측에 관한 중요성이 강조되고 있다. 이에 본 연구에서는 보다 정확한 기업부도예측을 위해 높은 예측력과 동시에 과적합화의 문제를 해결한다고 알려진 SVM(Support Vector Machine)을 기반으로 퍼지이론(fuzzy theory)을 활용해 입력변수를 확장하고, 유전자 알고리즘(GA, Genetic Algorithm)을 이용해 유사 혹은 유사최적의 입력변수집합과 파라미터를 탐색하는 새로운 융합모형을 제시한다. 제안모형의 유용성을 검증하기 위하여 H은행의 비외감 중공업 기업 데이터를 이용하여 실험을 수행하였으며, 비교모형으로는 로짓분석, 판별분석, 의사결정나무, 사례기반추론, 인공신경망, SVM을 선정하였다. 실험결과, 제안모형이 모든 비교모형들에 비해 우수한 예측력을 보이는 것으로 나타났다. 본 연구는 우수한 예측 성능을 가진 다기법 융합 모형을 새롭게 제안하여, 부도예측 분야에 학술적, 실무적으로 기여할 수 있을 것으로 기대된다.

• 지능정보연구
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• 제13권2호
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• pp.15-26
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• 2007

본 연구에서는 기존 Bagging Predictors에 수정을 가한 Modified Bagging Predictors를 이용하여 SOHO에 대한 부도예측 모델을 제시한다. 대기업 및 중소기업에 대한 기업부도예측 모델에 대한 많은 선행 연구가 있어왔지만 SOHO만의 기업부도 예측 모델에 관한 연구는 미비한 상태이다. 금융기관들의 대출 심사 시 대기업 및 중소기업과는 달리 SOHO에 대한 대출심사는 아직은 체계화되지 못한 채 신용정보점수 등의 단편적인 요소를 사용하고 있는 것이 현실이고 이에 따라 잘못된 대출로 인한 금융기관의 부실화를 초래할 위험성이 크다. 본 연구에서는 실제국내은행의 SOHO 대출 데이터 집합이 사용되었다. 먼저, 기업부도 예측 모델에서 우수하다고 연구되어진 인공신경망과 의사결정나무 추론 기법을 적용하여 보았지만 만족할 만한 성과를 이끌어내지 못하여, 기존 기업부도 예측 모델 연구에서 적용이 미비하였던 Bagging Predictors와 이를 개선한 Modified Bagging Predictors를 제시하고 이를 적용하여 보았다. 연구결과, SOHO 부도 예측에 있어서 본 연구에서 제시한 Modified Bagging Predictors가 인공신경망과 Bagging Predictors 등의 기존 기법에 비해서 성과가 향상됨을 알 수 있었다.

• Journal of Electrical Engineering and Technology
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• 제6권2호
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• pp.284-292
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• 2011

This paper presents a load-shedding scheme using the Talmud rule in islanded microgrid operation based on a multiagent system. Load shedding is an intentional load reduction to meet a power balance between supply and demand when supply shortages occur. The Talmud rule originating from the Talmud literature has been used in bankruptcy problems of finance, economics, and communications. This paper approaches the load-shedding problem as a bankruptcy problem. A load-shedding scheme is mathematically expressed based on the Talmud rule. For experiment of this approach, a multiagent system is constructed to operate test islanded microgrids autonomously. The suggested load-shedding scheme is tested on the test islanded microgrids based on the multiagent system. Results of the tests are discussed.

• Management Science and Financial Engineering
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• 제12권1호
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• pp.65-77
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• 2006

This study proposes a Bayesian dynamic model in a hierarchical way to assess the time-varying effect of risk factors on the likelihood of corporate bankruptcy. For the longitudinal data, we aim to describe dynamically evolving effects of covariates more articulately compared to the Generalized Estimating Equation approach. In the analysis, it is shown that the proposed model outperforms in terms of sensitivity and specificity. Besides, the usefulness of this study can be found from the flexibility in describing the dependence structure among time specific parameters and suitability for assessing the time effect of risk factors.

• 한국경영과학회지
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• 제33권2호
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• pp.1-16
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• 2008

The purpose of this paper is to propose a new binary classification method for predicting corporate failure based on genetic algorithm, and to validate its prediction power through empirical analysis. Establishing virtual companies representing bankrupt companies and non-bankrupt ones respectively, the proposed method measures the similarity between the virtual companies and the subject for prediction, and classifies the subject into either bankrupt or non-bankrupt one. The values of the classification variables of the virtual companies and the weights of the variables are determined by the proper model to maximize the hit ratio of training data set using genetic algorithm. In order to test the validity of the proposed method, we compare its prediction accuracy with ones of other existing methods such as multi-discriminant analysis, logistic regression, decision tree, and artificial neural network, and it is shown that the binary classification method we propose in this paper can serve as a premising alternative to the existing methods for bankruptcy prediction.

• 한국경영과학회지
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• 제26권1호
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• pp.109-116
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• 2001

The detection of corporate failures is a subject that has been particularly amenable to cross-sectional financial ratio analysis. In most of firms, however, the financial data are available over past years. Because of this, a model utilizing these longitudinal data could provide useful information on the prediction of bankruptcy. To correctly reflect the longitudinal and firm-specific data, the generalized linear model with assuming the first order AR(autoregressive) process is proposed. The method is motivated by the clinical research that several characteristics are measured repeatedly from individual over the time. The model is compared with several other predictive models to evaluate the performance. By using the financial data from manufacturing corporations in the Korea Stock Exchange (KSE) list, we will discuss some experiences learned from the procedure of sampling scheme, variable transformation, imputation, variable selection, and model evaluation. Finally, implications of the model with repeated measurement and future direction of research will be discussed.

• Journal of Information Technology Applications and Management
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• 제23권1호
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• pp.45-59
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• 2016

An ensemble classifier is a method that combines output of multiple classifiers. It has been widely accepted that ensemble classifiers can improve the prediction accuracy. Recently, ensemble techniques have been successfully applied to the bankruptcy prediction. Bagging and random subspace are the most popular ensemble techniques. Bagging and random subspace have proved to be very effective in improving the generalization ability respectively. However, there are few studies which have focused on the integration of bagging and random subspace. In this study, we proposed a new hybrid ensemble model to integrate bagging and random subspace method using genetic algorithm for improving the performance of the model. The proposed model is applied to the bankruptcy prediction for Korean companies and compared with other models in this study. The experimental results showed that the proposed model performs better than the other models such as the single classifier, the original ensemble model and the simple hybrid model.

• 한국IT서비스학회지
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• 제15권1호
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• pp.153-166
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• 2016

Ensemble classification is an approach that combines individually trained classifiers in order to improve prediction accuracy over individual classifiers. Ensemble techniques have been shown to be very effective in improving the generalization ability of the classifier. But base classifiers need to be as accurate and diverse as possible in order to enhance the generalization abilities of an ensemble model. Bagging is one of the most popular ensemble methods. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. In this study we proposed a new bagging variant ensemble model, Randomized Bagging (RBagging) for improving the standard bagging ensemble model. The proposed model was applied to the bankruptcy prediction problem using a real data set and the results were compared with those of the other models. The experimental results showed that the proposed model outperformed the standard bagging model.