재무부실화 예측을 위한 랜덤 서브스페이스 앙상블 모형의 최적화

• 영문명: Optimization of Random Subspace Ensemble for Bankruptcy Prediction
• 발행기관: 한국IT서비스학회
• 저자명: 민성환(Sung Hwan Min)
• 간행물 정보: 『한국IT서비스학회지』한국IT서비스학회지 제14권 제4호, 121~135쪽, 전체 15쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2015.12.30

영문 초록

Ensemble classification is to utilize multiple classifiers instead of using a single classifier. Recently ensemble classifiers have attracted much attention in data mining community. Ensemble learning techniques has been proved to be very useful for improving the prediction accuracy. Bagging, boosting and random subspace are the most popular ensemble methods. In random subspace, each base classifier is trained on a randomly chosen feature subspace of the original feature space. The outputs of different base classifiers are aggregated together usually by a simple majority vote. In this study, we applied the random subspace method to the bankruptcy problem. Moreover, we proposed a method for optimizing the random subspace ensemble. The genetic algorithm was used to optimize classifier subset of random subspace ensemble for bankruptcy prediction. This paper applied the proposed genetic algorithm based random subspace ensemble model to the bankruptcy prediction problem using a real data set and compared it with other models. Experimental results showed the proposed model outperformed the other models.

목차

Abstract

1. 서론

2. 앙상블 분류기

3. 연구 모형

4. 실험 설계

5. 실험 결과

6. 결론

References

키워드

Random Subspace Ensemble Bankruptcy Prediction Genetic Algorithms