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A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problems

  • Vijay Ingalalli
  • Sara Silva
  • Mauro Castelli
  • Leonardo Vanneschi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8599)

Abstract

Classification problems are of profound interest for the machine learning community as well as to an array of application fields. However, multi-class classification problems can be very complex, in particular when the number of classes is high. Although very successful in so many applications, GP was never regarded as a good method to perform multi-class classification. In this work, we present a novel algorithm for tree based GP, that incorporates some ideas on the representation of the solution space in higher dimensions. This idea lays some foundations on addressing multi-class classification problems using GP, which may lead to further research in this direction. We test the new approach on a large set of benchmark problems from several different sources, and observe its competitiveness against the most successful state-of-the-art classifiers.

Keywords

Random Forest Genetic Programming Mahalanobis Distance Parse Tree Percentage Accuracy 
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 2014

Authors and Affiliations

  • Vijay Ingalalli
    • 1
    • 2
    • 3
  • Sara Silva
    • 1
    • 4
    • 5
  • Mauro Castelli
    • 6
  • Leonardo Vanneschi
    • 6
  1. 1.INESC-IDLisbonPortugal
  2. 2.LIRMMMontpellierFrance
  3. 3.IRSTEAMontpellierFrance
  4. 4.LabMAg, FCULUniversity of LisbonLisbonPortugal
  5. 5.CISUCUniversidade de CoimbraCoimbraPortugal
  6. 6.ISEGIUniversidade Nova de LisboaLisbonPortugal

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