랜덤화 배깅을 이용한 재무 부실화 예측

• 영문명: Randomized Bagging for Bankruptcy Prediction
• 발행기관: 한국IT서비스학회
• 저자명: 민성환(Sung-Hwan Min)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제15권 제1호, 153~166쪽, 전체 14쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2016.03.30

영문 초록

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

1. 서 론

2. 재무 부실화 예측 모형

3. 연구 모형

4. 실험 설계

5. 실험 결과

6. 결 론

키워드

Bagging Bankruptcy Prediction Ensemble