Generating Redundant Features with Unsupervised Multi-Tree Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Lensen:2018:EuroGP,
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author = "Andrew Lensen and Bing Xue and Mengjie Zhang",
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title = "Generating Redundant Features with Unsupervised
Multi-Tree Genetic Programming",
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booktitle = "EuroGP 2018: Proceedings of the 21st European
Conference on Genetic Programming",
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year = "2018",
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month = "4-6 " # apr,
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editor = "Mauro Castelli and Lukas Sekanina and
Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez",
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series = "LNCS",
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volume = "10781",
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publisher = "Springer Verlag",
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address = "Parma, Italy",
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pages = "84--100",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-77552-4",
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DOI = "doi:10.1007/978-3-319-77553-1_6",
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abstract = "Recently, feature selection has become an increasingly
important area of research due to the surge in
high-dimensional datasets in all areas of modern life.
A plethora of feature selection algorithms have been
proposed, but it is difficult to truly analyse the
quality of a given algorithm. Ideally, an algorithm
would be evaluated by measuring how well it removes
known bad features. Acquiring datasets with such
features is inherently difficult, and so a common
technique is to add synthetic bad features to an
existing dataset. While adding noisy features is an
easy task, it is very difficult to automatically add
complex, redundant features. This work proposes one of
the first approaches to generating redundant features,
using a novel genetic programming approach. Initial
experiments show that our proposed method can
automatically create difficult, redundant features
which have the potential to be used for creating
high-quality feature selection benchmark datasets.",
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notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in
conjunction with EvoCOP2018, EvoMusArt2018 and
EvoApplications2018",
- }
Genetic Programming entries for
Andrew Lensen
Bing Xue
Mengjie Zhang
Citations