Backpropagation and Kohonen Self-Organizing Feature Map in Bankruptcy Prediction

Backpropagation and Kohonen Self-Organizing Feature Map in Bankruptcy Prediction

Kidong Lee (University of Incheon, South Korea), David Booth (Kent State University, USA) and Pervaiz Alam (Kent State University, USA)
Copyright: © 2004 |Pages: 14
DOI: 10.4018/978-1-59140-176-6.ch008
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Abstract

The back-propagation (BP) network and the Kohonen self-organizing feature map, selected as the representative types for the supervised and unsupervised artificial neural networks (ANN) 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. Advantages as well as limitations of the studied methods are also identified and discussed.

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