여러 가지 Inductive 방법에 대한 통합모델 개발과 그 실증적 유효성에 대한 연구 = The Development of Hybrid Model and Empirical Study for the Several Inductive Approaches

다국어 초록 (Multilingual Abstract)

This research investigates computer generated hybrid second-order model of two numerically based approaches to risk classification: discriminant analysis and neural networks. The hybrid second-order models are derived by rule induction using the ID3 and tested in the several different kinds of data. This new hybrid approach is designed to combine the high prediction accuracy and robustness of DA or NN with perspicuity of ID3. The hybrid model also eliminates the problem of contradictory inputs of ID3. After doing empirical test for the validity of hybrid model using small and medium companies' bankrupt data, hybrid model shows high perspicuity, high prediction accuracy for bankrupt, and simplicity for rules. The hybrid model also shows high performance regardless the type of data such as numeric data, non-numeric data, and combined data.