Neuro-genetic approach for bankruptcy prediction modeling

Kyung Shik Shin; Kyoung Jun Lee

doi:10.1007/978-3-540-30133-2_85

Neuro-genetic approach for bankruptcy prediction modeling

Kyung Shik Shin, Kyoung Jun Lee

Division of Business Administration

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Scopus citations

Abstract

Artificial neural network (ANN) modeling has become the dominant modeling paradigm for bankruptcy prediction. To further improve the neural network's prediction capability, the integration of the ANN models and the hybridization of ANN with relevant paradigms such as evolutionary computing has been demanded. This paper first attempted to apply neuro-genetic approach to bankruptcy prediction problem for finding optimal weights and confirmed that the approach can be a good methodology though it currently could not outperform the backpropagation learning algorithm. The result of this paper shows a possibility of neuro-genetic approach to bankruptcy prediction problem since the simple neuro-genetic approach produced a meaningful performance.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Mircea Gh. Negoita, Robert J. Howlett, Lakhmi C. Jain
Publisher	Springer Verlag
Pages	646-652
Number of pages	7
ISBN (Print)	9783540232063
DOIs	https://doi.org/10.1007/978-3-540-30133-2_85
State	Published - 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3214
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-540-30133-2_85

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Shin, K. S., & Lee, K. J. (2004). Neuro-genetic approach for bankruptcy prediction modeling. In M. G. Negoita, R. J. Howlett, & L. C. Jain (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 646-652). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3214). Springer Verlag. https://doi.org/10.1007/978-3-540-30133-2_85

Shin, Kyung Shik ; Lee, Kyoung Jun. / Neuro-genetic approach for bankruptcy prediction modeling. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Mircea Gh. Negoita ; Robert J. Howlett ; Lakhmi C. Jain. Springer Verlag, 2004. pp. 646-652 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Artificial neural network (ANN) modeling has become the dominant modeling paradigm for bankruptcy prediction. To further improve the neural network's prediction capability, the integration of the ANN models and the hybridization of ANN with relevant paradigms such as evolutionary computing has been demanded. This paper first attempted to apply neuro-genetic approach to bankruptcy prediction problem for finding optimal weights and confirmed that the approach can be a good methodology though it currently could not outperform the backpropagation learning algorithm. The result of this paper shows a possibility of neuro-genetic approach to bankruptcy prediction problem since the simple neuro-genetic approach produced a meaningful performance.",

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Shin, KS & Lee, KJ 2004, Neuro-genetic approach for bankruptcy prediction modeling. in MG Negoita, RJ Howlett & LC Jain (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3214, Springer Verlag, pp. 646-652. https://doi.org/10.1007/978-3-540-30133-2_85

Neuro-genetic approach for bankruptcy prediction modeling. / Shin, Kyung Shik; Lee, Kyoung Jun.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Mircea Gh. Negoita; Robert J. Howlett; Lakhmi C. Jain. Springer Verlag, 2004. p. 646-652 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3214).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AB - Artificial neural network (ANN) modeling has become the dominant modeling paradigm for bankruptcy prediction. To further improve the neural network's prediction capability, the integration of the ANN models and the hybridization of ANN with relevant paradigms such as evolutionary computing has been demanded. This paper first attempted to apply neuro-genetic approach to bankruptcy prediction problem for finding optimal weights and confirmed that the approach can be a good methodology though it currently could not outperform the backpropagation learning algorithm. The result of this paper shows a possibility of neuro-genetic approach to bankruptcy prediction problem since the simple neuro-genetic approach produced a meaningful performance.

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Shin KS, Lee KJ. Neuro-genetic approach for bankruptcy prediction modeling. In Negoita MG, Howlett RJ, Jain LC, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 646-652. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-30133-2_85

Neuro-genetic approach for bankruptcy prediction modeling

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