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BUSINESS FAILURE PREDICTION WITH SUPPORT VECTOR MACHINES AND NEURAL NETWORKS: A COMPARATIVE STUDY *
BibTeX
@MISC{Min_businessfailure,
author = {Jae H. Min and Young-chan Lee},
title = {BUSINESS FAILURE PREDICTION WITH SUPPORT VECTOR MACHINES AND NEURAL NETWORKS: A COMPARATIVE STUDY *},
year = {}
}
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Abstract
Bankruptcy prediction has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. ANN approaches have, however, suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learning technique, support vector machines (SVM), to the bankruptcy prediction problem in an attempt to provide a model with better explanatory power. To evaluate the prediction accuracy of SVM, we compare its performance with three-layer fully connected backpropagation neural networks (BNN). The experiment results show that SVM outperforms the BNN.
Keyphrases
comparative study prediction accuracy recent study ann approach bankruptcy prediction bankruptcy prediction problem new machine learning technique previous literature support vector machine traditional statistical one artificial neural network experiment result research interest explanatory power backpropagation neural network
