기업부실화 예측에 대한 앙상블 학습의 성과 비교 = A Performance Comparison of Ensembles in Bankruptcy Prediction

국문 초록 (Abstract)

본 연구는 기업부실화 예측을 위하여 활용되어 왔던 의사결정트리, 인공신경망 및 Support Vector Machine(SVM) 등의 기계학습에 대하여 앙상블 학습을 적용한 성과를 비교하였다. 실험 결과 단일 분류자에서는 SVM의 성과가 가장 우수하였으나, 앙상블 학습을 적용한 결과는 의사결정트리의 성과가 인공신경망 및 SVM에 비교하여 우수하였다. 성과 개선 측면에서도 의사결정트리 및 인공신경망의 경우에는 앙상블 학습으로 인한 성과 개선의 효과가 발생하였으나, SVM의 경우에는 앙상블 학습의 유의적인 성과 개선이 나타나지 않았다. 이러한 원인은 앙상블 분류자의 다중공선성에 기인하고 있으며, 이를 해결하기 위해서는 하위 앙상블 재선택 문제와 결합 문제가 동시에 고려되어야 함을 보여주었다.

다국어 초록 (Multilingual Abstract)

This paper performs an empirical comparison of three ensembles, each of which uses DT(Decision Trees), NN(Neural Networks) , and SVM(Support Vector Machine) as base classifiers on bankruptcy prediction. Experimental results show that SVM outperforms DT and NN, while DT ensemble has higher accuracy than NN and SVM ensembles. Ensemble learning can significantly improve the performance of DT and NN, but it does not work well on SVM. The problem of selection and combination of sub-ensemble to resolve multicollinearity among base classifiers of ensemble is to be considered for the performance improvement of NN and SVM ensembles.

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