Can Genetic Programming Do Manifold Learning Too?
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Lensen:2019:EuroGP,
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author = "Andrew Lensen and Bing Xue and Mengjie Zhang",
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title = "Can Genetic Programming Do Manifold Learning Too?",
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booktitle = "EuroGP 2019: Proceedings of the 22nd European
Conference on Genetic Programming",
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year = "2019",
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month = "24-26 " # apr,
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editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco",
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series = "LNCS",
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volume = "11451",
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publisher = "Springer Verlag",
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address = "Leipzig, Germany",
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pages = "114--130",
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organisation = "EvoStar, Species",
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note = "Best paper",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-030-16669-4",
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URL = "https://www.springer.com/us/book/9783030166694",
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DOI = "doi:10.1007/978-3-030-16670-0_8",
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size = "16 pages",
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abstract = "Exploratory data analysis is a fundamental aspect of
knowledge discovery that aims to find the main
characteristics of a dataset. Dimensionality reduction,
such as manifold learning, is often used to reduce the
number of features in a dataset to a manageable level
for human interpretation. Despite this, most manifold
learning techniques do not explain anything about the
original features nor the true characteristics of a
dataset. In this paper, we propose a genetic
programming approach to manifold learning called GP-MaL
which evolves functional mappings from a
high-dimensional space to a lower dimensional space
through the use of interpretable trees. We show that
GP-MaL is competitive with existing manifold learning
algorithms, while producing models that can be
interpreted and re-used on unseen data. A number of
promising future directions of research are found in
the process.",
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notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts
Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in
conjunction with EvoCOP2019, EvoMusArt2019 and
EvoApplications2019",
- }
Genetic Programming entries for
Andrew Lensen
Bing Xue
Mengjie Zhang
Citations