View : 9 Download: 0

Bankruptcy prediction modeling using multiple neural network models

Title
Bankruptcy prediction modeling using multiple neural network models
Authors
Shin K.-S.Lee K.J.
Ewha Authors
신경식
SCOPUS Author ID
신경식scopus
Issue Date
2004
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN
0302-9743JCR Link
Citation
vol. 3214, pp. 668 - 674
Indexed
SCOPUS scopus
Abstract
The primary goal of this paper is to get over the limitations of single neural network models through model integration so as to increase the accuracy of bankruptcy prediction. We take the closeness of the output value to either 0 or 1 as the model's confidence in its prediction as to whether or not a company is going to bankrupt. In case where multiple models yield conflicting prediction results, our integrated model takes the output value of the highest confidence as the final output. The output of the confidence-based integration approach significantly increases the prediction performance. The results of composite prediction suggest that the proposed approach will offer improved performance in business classification problems by integrating case-specific knowledge with the confidence information and general knowledge with the multi-layer perceptron's generalization capability. © Springer-Verlag 2004.
Appears in Collections:
경영대학 > 경영학전공 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE