Application of Feature Extractive Algorithm to Bankruptcy Prediction

Authors:
Andreas Charitou;Froso Kaourou
Affiliations:
-;-
Venue:
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Year:
2000

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Clustering and visualization of bankruptcy trajectory using self-organizing map

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Abstract

This study uses the feature selection algorithm proposed by Setiono and Liu to select the most relevant features for the bankruptcy prediction problem. The method uses a feedforward neural network with one hidden layer to decide which features to be removed. Our data consists of 139 matched pair of bankrupt and non-bankrupt U.S. firms for the period 1983-1994. The results of this study indicate that the final neural network obtained with reduced number of inputs gives significantly better prediction results than the one that uses all initial features.