Feature Selection Using Geometric Semantic Genetic Programming

• Autor(a) principal: Rosa, G. H. [UNESP]
• Data de Publicação: 2017
• Outros Autores: Papa, J. P. [UNESP], Papa, L. P., Ochoa, G.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1145/3067695.3076020; http://hdl.handle.net/11449/210101
• topic: Feature selection; Geometric Semantic Genetic Programming

Feature selection concerns the task of finding the subset of features that are most relevant to some specific problem in the context of machine learning. During the last years, the problem of feature selection has been modeled as an optimization task, where the idea is to find the subset of features that maximize some fitness function, which can be a given classifier's accuracy or even some measure concerning the samples' separability in the feature space, for instance. In this paper, we introduced Geometric Semantic Genetic Programming (GSGP) in the context of feature selection, and we experimentally showed it can work properly with both conic and non-conic fitness landscapes.