Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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Impact of Ensemble Member Size on Confidence-based Selection in Bankruptcy Prediction

부도예측을 위한 확신 기반의 선택 접근법에서 앙상블 멤버 사이즈의 영향에 관한 연구

  • 김나라 (이화여자대학교 경영학과) ;
  • 신경식 (이화여자대학교 경영학과) ;
  • 안현철 (국민대학교 경영대학 경영정부학부)
  • Received : 2013.05.22
  • Accepted : 2013.06.19
  • Published : 2013.06.30
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Abstract

The prediction model is the main factor affecting the performance of a knowledge-based system for bankruptcy prediction. Earlier studies on prediction modeling have focused on the building of a single best model using statistical and artificial intelligence techniques. However, since the mid-1980s, integration of multiple techniques (hybrid techniques) and, by extension, combinations of the outputs of several models (ensemble techniques) have, according to the experimental results, generally outperformed individual models. An ensemble is a technique that constructs a set of multiple models, combines their outputs, and produces one final prediction. The way in which the outputs of ensemble members are combined is one of the important issues affecting prediction accuracy. A variety of combination schemes have been proposed in order to improve prediction performance in ensembles. Each combination scheme has advantages and limitations, and can be influenced by domain and circumstance. Accordingly, decisions on the most appropriate combination scheme in a given domain and contingency are very difficult. This paper proposes a confidence-based selection approach as part of an ensemble bankruptcy-prediction scheme that can measure unified confidence, even if ensemble members produce different types of continuous-valued outputs. The present experimental results show that when varying the number of models to combine, according to the creation type of ensemble members, the proposed combination method offers the best performance in the ensemble having the largest number of models, even when compared with the methods most often employed in bankruptcy prediction.

부도예측을 위한 지식기반시스템에서 모델은 실적에 영향을 끼치는 주요한 요인이다. 예측 모형의 개발에 있어 초기 연구들은 통계기법 및 인공지능기법들을 이용하여 최고 실적을 가지는 단일 모델을 만드는데 주력하였다. 1980년대 중반 이후에는 다수 기술의 통합(하이브리드), 더 나아가, 다수 모델의 결과의 결합(앙상블) 기법이 수많은 실험에서 개별 모델들보다 더 나은 결과를 보여왔다. 다수 모델들의 출력값들을 결합하여 한 개의 최종 예측값을 산출하는 앙상블 모델링에서 결합기법은 앙상블의 예측 정확도에 영향을 끼치는 중요한 이슈이다. 본 논문은 부도예측을 위한 앙상블 결합기법으로서 앙상블 멤버들이 다른 유형의 연속형 수치 출력값들을 산출하더라도 통일된 확신을 측정할 수 있는 확신 기반의 선택 접근법을 제안하고 이에 대한 앙상블 멤버 사이즈의 영향을 연구하였다. 실험 결과는 앙상블 멤버들의 생성 타입에 따라 결합하는 모델 개수를 변화시켰을 때 가장 많은 기본 모델들을 가지는 앙상블에서의 제안 결합기법이 부도예측에 가장 자주 사용되는 다른 방법들에 비해서도 가장 높은 실적을 가진다는 것을 보였다.

Keywords

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