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:1.8010
- @InProceedings{daVeigaCabral:2011:NaBIC,
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author = "Rafael {da Veiga Cabral} and Eduardo J. Spinosa",
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title = "Evaluating an outlier generation method for training
tree-based Genetic Programming applied to one-class
classification",
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booktitle = "Third World Congress on Nature and Biologically
Inspired Computing (NaBIC 2011)",
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year = "2011",
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month = "19-21 " # oct,
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pages = "395--400",
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address = "Salamanca",
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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.",
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keywords = "genetic algorithms, genetic programming, artificial
outliers, outlier generation method, supervised
classification problems, tree based genetic
programming, learning (artificial intelligence)",
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DOI = "doi:10.1109/NaBIC.2011.6089468",
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notes = "Also known as \cite{6089468}",
- }
Genetic Programming entries for
Rafael Valente Veiga
Eduardo J Spinosa
Citations