A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms

Authors:
Kidong Lee;David Booth;Pervaiz Alam
Affiliations:
College of Business Administration, University of Incheon, Incheon 402-749, South Korea;Department of Management and Information system, College of Business Administration, Kent State University, Kent, OH 44242, USA;Department of Accounting, College of Business Administration, Kent State University, Kent, OH 44242, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2005

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Abstract

In this study, two learning paradigms of neural networks, supervised versus unsupervised, are compared using their representative types. The back-propagation (BP) network and the Kohonen self-organizing feature map, selected as the representative type for supervised and unsupervised neural networks, respectively, are compared in terms of prediction accuracy in the area of bankruptcy prediction. Discriminant analysis and logistic regression are also performed to provide performance benchmarks. The findings suggest that the BP network is a better choice when a target vector is available.