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A genetic algorithm for discriminant analysis

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  • Daniel Conway
  • A. Victor Cabot
  • M.A. Venkataramanan

Abstract

In this paper we propose a genetic algorithm for discriminant analysis. The genetic fitness function uses duality principles of mathematical programming to solve the linear discriminant problem. The genetic method performed well in empirical testing and also provides the reasoning power behind the mathematical programming models. Copyright Kluwer Academic Publishers 1998

Suggested Citation

  • Daniel Conway & A. Victor Cabot & M.A. Venkataramanan, 1998. "A genetic algorithm for discriminant analysis," Annals of Operations Research, Springer, vol. 78(0), pages 71-82, January.
    • Handle: RePEc:spr:annopr:v:78:y:1998:i:0:p:71-82:10.1023/a:1018958318666
    DOI: 10.1023/A:1018958318666
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    Cited by:

    1. Mingue Sun, 2009. "Liquidity Risk and Financial Competition: A Mixed Integer Programming Model for Multiple-Class Discriminant Analysis," Working Papers 0102, College of Business, University of Texas at San Antonio.
    2. Mingue Sun, 2009. "Liquidity Risk and Financial Competition: A Mixed Integer Programming Model for Multiple-Class Discriminant Analysis," Working Papers 0102, College of Business, University of Texas at San Antonio.
    3. Zopounidis, Constantin & Doumpos, Michael, 2002. "Multicriteria classification and sorting methods: A literature review," European Journal of Operational Research, Elsevier, vol. 138(2), pages 229-246, April.

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