Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis

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29 Citations (Scopus)

Abstract

There are several data based methods in the field of artificial intelligence which are nowadays frequently used for analyzing classification problems in the context of medical applications. As we show in this paper, the application of enhanced evolutionary computation techniques to classification problems has the potential to evolve classifiers of even higher quality than those trained by standard machine learning methods. On the basis of five medical benchmark classification problems taken from the UCI repository as well as the Melanoma data set (prepared by members of the Department of Dermatology of the Medical University Vienna) we document that the enhanced genetic programming approach presented here is able to produce comparable or even better results than linear modeling methods, artificial neural networks, kNN classification, support vector machines and also various genetic programming approaches.

Original languageEnglish
Pages (from-to)111-140
Number of pages30
JournalGenetic Programming and Evolvable Machines
Volume10
Issue number2
DOIs
Publication statusPublished - Jun 2009

Keywords

  • Adaptation/self-adaptation
  • Classifier systems
  • Data mining
  • Empirical study
  • Genetic programming
  • Medicine

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