Evaluating an outlier generation method for training tree-based Genetic Programming applied to one-class classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{daVeigaCabral:2011:NaBIC,

• author = "Rafael {da Veiga Cabral} and Eduardo J. Spinosa",
• title = "Evaluating an outlier generation method for training tree-based Genetic Programming applied to one-class classification",
• booktitle = "Third World Congress on Nature and Biologically Inspired Computing (NaBIC 2011)",
• year = "2011",
• month = "19-21 " # oct,
• pages = "395--400",
• address = "Salamanca",
• abstract = "Genetic Programming (GP) has been successfully applied to supervised classification problems. This work evaluates a tree-based GP implementation in a one-class classification scenario, using artificial outliers generated by a promising method recently developed by Banhalmi et al. The proposed approach does not require the use of certain techniques employed by related works, thus providing a simpler yet effective strategy for one-class classification based on GP. Experiments presented herein explore parameter sensitivity of Banhalmi outlier generation method and compare the proposed approach to previously published results obtained by others one-class classifiers like v-SVM, one-class SVM and GMM.",
• keywords = "genetic algorithms, genetic programming, artificial outliers, outlier generation method, supervised classification problems, tree based genetic programming, learning (artificial intelligence)",
• DOI = "doi:10.1109/NaBIC.2011.6089468",
• notes = "Also known as \cite{6089468}",

}

Genetic Programming entries for Rafael Valente Veiga Eduardo J Spinosa

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