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Rough Sets Theory and Multivariate Data Analysis in Classification Problems: a Simulation Study

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Computational Intelligence in Economics and Finance

Part of the book series: Advanced Information Processing ((AIP))

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Abstract

The classification of a set of objects into predefined homogenous groups is a problem with major practical interest in many fields. Over the past two decades several non-parametric approaches have been developed to address the classification problem, originating from several scientific fields. This paper is focused on a rule induction approach based on the rough sets theory and the investigation of its performance as opposed to traditional multivariate statistical classification procedures, namely the linear discriminant analysis, the quadratic discriminant analysis and the logit analysis. For this purpose an extensive Monte Carlo simulation is conducted to examine the performance of these methods under different data conditions.

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Author information

Authors and Affiliations

  1. Financial Engineering Laboratory, Department of Production Engineering and Management, Technical University of Crete, University Campus, 73100, Chania, Greece

    Michael Doumpos & Constantin Zopounidis

Authors
  1. Michael Doumpos
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  2. Constantin Zopounidis
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Editors and Affiliations

  1. AI-ECON Research Center Department of Economics, National Chengchi University, 11623, Taipei, Taiwan

    Shu-Heng Chen

  2. Department of Electrical and Computer Science, Duke University, P.O. 90291, 27708-0291, Durham, NC, USA

    Paul P. Wang

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© 2004 Springer-Verlag Berlin Heidelberg

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Doumpos, M., Zopounidis, C. (2004). Rough Sets Theory and Multivariate Data Analysis in Classification Problems: a Simulation Study. In: Chen, SH., Wang, P.P. (eds) Computational Intelligence in Economics and Finance. Advanced Information Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-06373-6_4

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  • DOI: https://doi.org/10.1007/978-3-662-06373-6_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-07902-3

  • Online ISBN: 978-3-662-06373-6

  • eBook Packages: Springer Book Archive

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