A General Feature Engineering Wrapper for Machine Learning Using epsilon-Lexicase Survival
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{LaCava:2017:EuroGP,
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author = "William {La Cava} and Jason Moore",
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title = "A General Feature Engineering Wrapper for Machine
Learning Using epsilon-Lexicase Survival",
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booktitle = "EuroGP 2017: Proceedings of the 20th European
Conference on Genetic Programming",
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year = "2017",
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month = "19-21 " # apr,
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editor = "Mauro Castelli and James McDermott and
Lukas Sekanina",
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series = "LNCS",
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volume = "10196",
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publisher = "Springer Verlag",
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address = "Amsterdam",
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pages = "80--95",
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organisation = "species",
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keywords = "genetic algorithms, genetic programming, Feature
selection, Representation learning, Regression",
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isbn13 = "978-3-319-55695-6",
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URL = "https://cavalab.org/assets/papers/La_Cava_and_Moore_-_2017_-_A_General_Feature_Engineering_Wrapper_for_Machine_.pdf",
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DOI = "doi:10.1007/978-3-319-55696-3_6",
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code_url = "https://github.com/lacava/few",
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size = "16 pages",
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abstract = "We propose a general wrapper for feature learning that
interfaces with other machine learning methods to
compose effective data representations. The proposed
feature engineering wrapper (FEW) uses genetic
programming to represent and evolve individual features
tailored to the machine learning method with which it
is paired. In order to maintain feature diversity,
e-lexicase survival is introduced, a method based on
epsilon-lexicase selection. This survival method
preserves semantically unique individuals in the
population based on their ability to solve difficult
subsets of training cases, thereby yielding a
population of uncorrelated features. We demonstrate FEW
with five different off-the-shelf machine learning
methods and test it on a set of real-world and
synthetic regression problems with dimensions varying
across three orders of magnitude. The results show that
FEW is able to improve model test predictions across
problems for several ML methods. We discuss and test
the scalability of FEW in comparison to other feature
composition strategies, most notably polynomial feature
expansion.",
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notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held
inconjunction with EvoCOP2017, EvoMusArt2017 and
EvoApplications2017",
- }
Genetic Programming entries for
William La Cava
Jason H Moore
Citations