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Neuro-Genetic Approach for Bankruptcy Prediction Modeling

  • Conference paper
Knowledge-Based Intelligent Information and Engineering Systems (KES 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3214))

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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 neurogenetic 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 neurogenetic approach to bankruptcy prediction problem since the simple neurogenetic approach produced a meaningful performance.

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Author information

Authors and Affiliations

  1. College of Business Administration, Ewha Womans University, Daehyun-dong, Seodaemun-ku, Seoul, Korea

    Kyung-shik Shin

  2. School of Business, Kyung Hee University, Hoegi-Dong, Dongdaemun-Ku, Seoul, Korea

    Kyoung Jun Lee

Authors
  1. Kyung-shik Shin
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  2. Kyoung Jun Lee
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Editor information

Editors and Affiliations

  1. KES International, 2nd Floor, 145-157 St John Street, EC1V 4PY, London, United Kingdom

    Mircea Gh. Negoita

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2004 Springer-Verlag Berlin Heidelberg

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Shin, Ks., Lee, K.J. (2004). Neuro-Genetic Approach for Bankruptcy Prediction Modeling. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_85

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  • DOI: https://doi.org/10.1007/978-3-540-30133-2_85

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23206-3

  • Online ISBN: 978-3-540-30133-2

  • eBook Packages: Springer Book Archive

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