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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationApplications of Evolutionary Computation - 16th European Conference, EvoApplications 2013, Proceedings
Pages334-343
Number of pages10
DOIs
Publication statusPublished - 5 Apr 2013
Event16th European Conference on Applications of Evolutionary Computation, EvoApplications 2013 - Vienna, Austria
Duration: 3 Apr 20135 Apr 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7835 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th European Conference on Applications of Evolutionary Computation, EvoApplications 2013
CountryAustria
CityVienna
Period3/04/135/04/13

Fingerprint Dive into the research topics of 'Land cover/land use multiclass classification using GP with geometric semantic operators'. Together they form a unique fingerprint.

Cite this