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Direct Minimization of Error Rates in Multivariate Classification

Author

Listed:
  • Michael C. Röhl

    (Königstein/Ts.)

  • Claus Weihs

    (Universität Dortmund)

  • Winfried Theis

    (Universität Dortmund)

Abstract

Summary We propose a computer intensive method for linear dimension reduction that minimizes the classification error directly. Simulated annealing (Bohachevsky et al. 1986), a modern optimization technique, is used to solve this problem effectively. This approach easily allows user preferences to be incorporated by means of penalty terms. Simulations and a real world example demonstrate the superiority of this optimal classification to classical discriminant analysis (McLachlan 1992). Special emphasis is given to the case when discriminant analysis collapses.

Suggested Citation

  • Michael C. Röhl & Claus Weihs & Winfried Theis, 2002. "Direct Minimization of Error Rates in Multivariate Classification," Computational Statistics, Springer, vol. 17(1), pages 29-46, March.
    • Handle: RePEc:spr:compst:v:17:y:2002:i:1:d:10.1007_s001800200089
    DOI: 10.1007/s001800200089
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    References listed on IDEAS

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    1. Polzehl, Jorg, 1995. "Projection pursuit discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 20(2), pages 141-157, August.
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    Citations

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    Cited by:

    1. Enache, Daniel & Weihs, Claus, 2004. "Importance Assessment of Correlated Predictors in Business Cycles Classification," Technical Reports 2004,66, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Luebke, Karsten & Weihs, Claus, 2004. "Generation of prediction optimal projection on latent factors by a stochastic search algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 297-310, September.
    3. Karsten Luebke & Claus Weihs, 2011. "Linear dimension reduction in classification: adaptive procedure for optimum results," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(3), pages 201-213, October.

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