Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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Bankruptcy Prediction using Fuzzy Neural Networks

퍼지신경망을 이용한 기업부도예측

  • 김경재 (한국과학기술원 테크노경영연구소) ;
  • 한인구 (한국과학기술원 테크노경영대학원)
  • Published : 2001.06.01
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Abstract

This study proposes bankruptcy prediction model using fuzzy neural networks. Neural networks offer preeminent learning ability but they are often confronted with the inconsistent and unpredictable performance for noisy financial data. The existence of continuous data and large amounts of records may pose a challenging task to explicit concepts extraction from the raw data due to the huge data space determined by continuous input variables. The attempt to solve this problem is to transform each input variable in a way which may make it easier fur neural network to develop a predictive relationship. One of the methods selected for this is to map each continuous input variable to a series of overlapping fuzzy sets. Appropriately transforming each of the inputs into overlapping fuzzy membership sets provides an isomorphic mapping of the data to properly constructed membership values, and as such, no information is lost. In addition, it is easier far neural network to identify and model high-order interactions when the data is transformed in this way. Experimental results show that fuzzy neural network outperforms conventional neural network for the prediction of corporate bankruptcy.

본 연구에서는 퍼지신경망을 이용한 기업부실예측모형을 제안한다. 신경망은 탁월한 학습능력을 가진 것으로 알려져 있으나, 잡음이 심한 재무자료에 대해서는 종종 일관되지 못하고 기대에 미치지 못하는 예측성과를 보인다. 이는 연속형의 형태를 지닌 독립변수와 과다한 양의 원자료로부터 예측에 필요한 일정한 패턴을 찾기가 어렵기 때문이다. 이러한 문제점은 예측모형에서의 독립변수와 종속변수간의 인과관계를 신경망이 용이하게 찾아낼 수 있도록 독립변수의 형태를 변환함으로써 해결한 수 있다. 이러한 해결방법의 하나는 기존 신경망에 퍼지집합의 개념을 적용하여 신경망 학습에 사용될 자료를 퍼지화하고 이를 신경망에 학습시키는 것이다 입력자료를 퍼지화 함으로써 정보의 손실 없이도 신경망이 자료 내의 복잡한 관계를 용이하게 학습하는 것이 가능하다. 본 연구에서 제안된 퍼지신경망을 기업부도예측에 적용한 결과, 퍼지신경망이 기존의 신경망보다 우월한 예측성과를 나타내었다.

Keywords

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