랜덤화 배깅을 이용한 재무 부실화 예측 = Randomized Bagging for Bankruptcy Prediction

다국어 초록 (Multilingual Abstract)

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.

목차 (Table of Contents)

• 1. 서 론
• 2. 재무 부실화 예측 모형
• 3. 연구 모형
• 4. 실험 설계
• 5. 실험 결과
• 6. 결 론

참고문헌 (Reference)

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2 최하나, "앙상블 SVM 모형을 이용한 기업 부도 예측" 한국데이터정보과학회 24 (24): 1113-1125, 2013

3 김진우, "신용카드 대손회원 예측을 위한 SVM 모형" 한국IT서비스학회 11 (11): 233-250, 2012

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