Solving multiclass classification problems by genetic programming

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Abstract

In this paper a multiclass classification problem solving technique based on genetic programming is presented. Classification algorithms are designed to learn a function which maps a vector of object features into one of several classes; this is done by analyzing a set of input-output examples of the function (also called "training samples"). Here we present a method based on the theory of genetic algorithms and genetic programming that interprets classification problems as optimization problems. The major aspects presented in this paper are suitable genetic operators for this problem class (mainly the creation of new hypotheses by merging already existing ones and their detailed evaluation) we have designed and implemented. We define a novel function for measuring a classificator model's quality that takes into account several different features of the model to be evaluated; an extended version of ROC curves that can be applied not only to two-class-classification but also to multiclass classification problems, is also presented. The experimental part of the paper documents the ability of this method to yield very satisfying results; selected results achieved for two classification benchmark problems are discussed.

Original languageEnglish
Title of host publicationWMSCI 2005 - The 9th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings
PublisherInternational Institute of Informatics and Systemics
Pages48-53
Number of pages6
ISBN (Print)9806560531, 9789806560536
Publication statusPublished - 2005
Event9th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2005 - Orlando, FL, United States
Duration: 10 Jul 200513 Jul 2005

Publication series

NameWMSCI 2005 - The 9th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings
Volume1

Conference

Conference9th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2005
Country/TerritoryUnited States
CityOrlando, FL
Period10.07.200513.07.2005

Keywords

  • Classification
  • Data mining
  • Genetic programming
  • Knowledge representation

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