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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Land Cover/Land Use Multiclass Classification Using GP with Geometric Semantic Operators
verfasst von : Mauro Castelli, Sara Silva, Leonardo Vanneschi, Ana Cabral, Maria J. Vasconcelos, Luís Catarino, João M. B. Carreiras
Erschienen in: Applications of Evolutionary Computation
Verlag: Springer Berlin Heidelberg
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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.