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A genetic-based hybrid approach to corporate failure prediction

A genetic-based hybrid approach to corporate failure prediction This paper proposes a genetic-based hybrid approach to predict the possibility of corporate failure. We use Genetic Algorithm (GA) to select the critical variables set and optimise the weight of each classifier for integrating the best features of several classification approaches (such as discriminant analysis, logistic regression and neural networks) in order to enhance prediction results. GA with nonlinear searching capabilities extracts more critical feature variables if compared with the Stepwise Method. This means that the undesirable variables for classification models will be cleaned out by GA. In addition, our experimental results show that this hybrid approach obtains better prediction performance than when using a single approach effectively. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Electronic Finance Inderscience Publishers

A genetic-based hybrid approach to corporate failure prediction

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1746-0069
eISSN
1746-0077
DOI
10.1504/IJEF.2008.017543
Publisher site
See Article on Publisher Site

Abstract

This paper proposes a genetic-based hybrid approach to predict the possibility of corporate failure. We use Genetic Algorithm (GA) to select the critical variables set and optimise the weight of each classifier for integrating the best features of several classification approaches (such as discriminant analysis, logistic regression and neural networks) in order to enhance prediction results. GA with nonlinear searching capabilities extracts more critical feature variables if compared with the Stepwise Method. This means that the undesirable variables for classification models will be cleaned out by GA. In addition, our experimental results show that this hybrid approach obtains better prediction performance than when using a single approach effectively.

Journal

International Journal of Electronic Finance – Inderscience Publishers

Published: Jan 1, 2008

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