Ensemble prediction of commercial bank failure through diversification of input features

Shin S. , Lee K. C. , Kilic S. B.

19th Australian Joint Conference on Artificial Intelligence, Hobart, Australia, 4 - 08 December 2006, vol.4304, pp.887-888 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 4304
  • Doi Number: 10.1007/11941439-93
  • City: Hobart
  • Country: Australia
  • Page Numbers: pp.887-888


As primary focus of banking regulation and supervision is being shifted toward internal risk management for all commercial banks, financial data mining task such as an early warning of bank failure becomes more critical than ever. In this study, we examine the effect of variable selection methods for intelligent bankruptcy prediction models. Moreover, an augmented stacked generalizer that utilizes diversified feature subsets during its learning phase is suggested as an effective ensemble method for promoting independencies among base prediction models. Empirical results show that the augmented stacked generalizer significantly improves overall predictability by reducing the more costly type-I error rate compared against both popular bagging and standard stacking procedures.