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Analysis of Selected Evolutionary Algorithms in Feature Selection and Parameter Optimization for Data Based Tumor Marker Modeling

  • Stephan M. Winkler
  • Michael Affenzeller
  • Gabriel Kronberger
  • Michael Kommenda
  • Stefan Wagner
  • Witold Jacak
  • Herbert Stekel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6927)

Abstract

In this paper we report on the use of evolutionary algorithms for optimizing the identification of classification models for selected tumor markers. Our goal is to identify mathematical models that can be used for classifying tumor marker values as normal or as elevated; evolutionary algorithms are used for optimizing the parameters for learning classification models. The sets of variables used as well as the parameter settings for concrete modeling methods are optimized using evolution strategies and genetic algorithms. The performance of these algorithms is analyzed as well as the population diversity progress. In the empirical part of this paper we document modeling results achieved for tumor markers CA 125 and CYFRA using a medical data base provided by the Central Laboratory of the General Hospital Linz; empirical tests are executed using HeuristicLab.

Keywords

Support Vector Machine Feature Selection Tumor Marker Evolutionary Algorithm Elevated Tumor Marker 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Stephan M. Winkler
    • 1
  • Michael Affenzeller
    • 1
  • Gabriel Kronberger
    • 1
  • Michael Kommenda
    • 1
  • Stefan Wagner
    • 1
  • Witold Jacak
    • 1
  • Herbert Stekel
    • 2
  1. 1.Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and MediaUpper Austrian University of Applied SciencesHagenbergAustria
  2. 2.Central LaboratoryGeneral Hospital LinzLinzAustria

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