Land cover/land use multiclass classification using GP with geometric semantic operators

  • Authors:

  • Mauro Castelli;Sara Silva;Leonardo Vanneschi;Ana Cabral;Maria J. Vasconcelos;Luís Catarino;João M. B. Carreiras

  • Affiliations:

  • INESC-ID, IST, Universidade Técnica de Lisboa, Lisboa, Portugal, ISEGI, Universidade Nova de Lisboa, Lisboa, Portugal;INESC-ID, IST, Universidade Técnica de Lisboa, Lisboa, Portugal, CISUC, Universidade de Coimbra, Coimbra, Portugal;ISEGI, Universidade Nova de Lisboa, Lisboa, Portugal, INESC-ID, IST, Universidade Técnica de Lisboa, Lisboa, Portugal;Instituto de Investigação Científica Tropical, Lisboa, Portugal;Instituto de Investigação Científica Tropical, Lisboa, Portugal;Instituto de Investigação Científica Tropical, Lisboa, Portugal, CIBIO, Universidade do Porto, Vairão, Portugal;Instituto de Investigação Científica Tropical, Lisboa, Portugal

  • Venue:
  • EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
  • Year:
  • 2013

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Abstract

Multiclass classification is a common requirement of many land cover/land use applications, one of the pillars of land science studies. Even though genetic programming has been applied with success to a large number of applications, it is not particularly suited for multiclass classification, thus limiting its use on such studies. In this paper we take a step forward towards filling this gap, investigating the performance of recently defined geometric semantic operators on two land cover/land use multiclass classification problems and also on a benchmark problem. Our results clearly indicate that genetic programming using the new geometric semantic operators outperforms standard genetic programming for all the studied problems, both on training and test data.