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Predicting Business Failure with a Case-Based Reasoning Approach

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

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3215))

Abstract

Accurately identifying potentially failing companies is beneficial to stakeholders of the companies. This paper uses a case-based reasoning (CBR) approach to predict business failure. CBR is a problem-solving paradigm that uses past experiences to solve new problems. Nearest neighbor (NN) is a common CBR algorithm for retrieving similar cases, but its similarity function is sensitive to irrelevant attributes. To ensure the effective retrieval of similar cases, statistical evaluations are used for automatically assigning relative importance of the attributes for the NN retrieval. The results of this study indicate that this approach is an effective and competitive alternative to predict business failure in a comprehensible manner.

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Author information

Authors and Affiliations

  1. School of Business Information Technology, RMIT Business, RMIT University, GPO Box 2476V, Melbourne, Victoria, 3001, Australia

    Angela Y. N. Yip

Authors
  1. Angela Y. N. Yip
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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, SA, Australia

    Lakhmi C. Jain

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© 2004 Springer-Verlag Berlin Heidelberg

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Yip, A.Y.N. (2004). Predicting Business Failure with a Case-Based Reasoning Approach. 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 3215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30134-9_89

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  • DOI: https://doi.org/10.1007/978-3-540-30134-9_89

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23205-6

  • Online ISBN: 978-3-540-30134-9

  • eBook Packages: Springer Book Archive

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