Feature Selection Using Geometric Semantic Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Rosa:2017:GECCO,

• author = "G. H. Rosa and J. P. Papa and L. P. Papa",
• title = "Feature Selection Using Geometric Semantic Genetic Programming",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion",
• series = "GECCO '17",
• year = "2017",
• isbn13 = "978-1-4503-4939-0",
• address = "Berlin, Germany",
• pages = "253--254",
• size = "2 pages",
• URL = "http://doi.acm.org/10.1145/3067695.3076020",
• DOI = "doi:10.1145/3067695.3076020",
• acmid = "3076020",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, feature selection, geometric semantic genetic programming",
• month = "15-19 " # jul,
• abstract = "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 modelled 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.",
• notes = "Also known as \cite{Rosa:2017:FSU:3067695.3076020} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)",

}

Genetic Programming entries for Gustavo Rosa Joao Paulo Papa Luciene Patrici Papa

Citations