@ARTICLE { Rsds1766,
author = {Ahn, B. S. and Cho, S.S. and Kim, C.Y.}, 
title = {The integrated methodology of rough set theory and artificial neural network for business failure prediction}, 
journal = {Expert Systems with Applications}, 
number = 	{(no. 2)}, 
volume = 18, 
pages = {65-74}, 
publisher = {Elsevier Science Inc.}, 
address = {Amsterdam, The Netherlands}, 
month = {February}, 
year = 2000, 
abstract = {This paper proposes a hybrid intelligent system that predicts the failure of firms based on the past financial performance data, combining rough set approach and neural network. We can get reduced information table, which implies that the number of evaluation criteria such as financial ratios and qualitative variables is reduced with no information loss through rough set approach. And then, this reduced information is used to develop classification rules and train neural network to infer appropriate parameters. The rules developed by rough set analysis show the best prediction accuracy if a case does match any of the rules. The rationale of our hybrid system is using rules developed by rough sets for an object that matches any of the rules and neural network for one that does not match any of them. The effectiveness of our methodology was verified by experiments comparing traditional discriminant analysis and neural network approach with our hybrid approach. For the experiment, the financial data of 2400 Korean firms during the period 1994-1997 were selected, and for the validation, k-fold validation was used.}, 
keywords = {business failure prediction, rough sets, }, 
}