RANSAC-GP: Dealing with Outliers in Symbolic Regression with Genetic Programming
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gp-bibliography.bib Revision:1.7964
- @InProceedings{Lopez:2017:EuroGP,
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author = "Uriel Lopez and Leonardo Trujillo and
Yuliana Martinez and Pierrick Legrand and Enrique Naredo and
Sara Silva",
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title = "{RANSAC-GP}: Dealing with Outliers in Symbolic
Regression with Genetic Programming",
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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 = "114--130",
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organisation = "species",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-55695-6",
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DOI = "doi:10.1007/978-3-319-55696-3_8",
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abstract = "Genetic programming (GP) has been shown to be a
powerful tool for automatic modelling and program
induction. It is often used to solve difficult symbolic
regression tasks, with many examples in real-world
domains. However, the robustness of GP-based approaches
has not been substantially studied. In particular, the
present work deals with the issue of outliers, data in
the training set that represent severe errors in the
measuring process. In general, a datum is considered an
outlier when it sharply deviates from the true
behaviour of the system of interest. GP practitioners
know that such data points usually bias the search and
produce inaccurate models. Therefore, this work
presents a hybrid methodology based on the RAndom
SAmpling Consensus (RANSAC) algorithm and GP, which we
call RANSAC- GP. RANSAC is an approach to deal with
outliers in parameter estimation problems, widely used
in computer vision and related fields. On the other
hand, this work presents the first application of
RANSAC to symbolic regression with GP, with impressive
results. The proposed algorithm is able to deal with
extreme amounts of contamination in the training set,
evolving highly accurate models even when the amount of
outliers reaches 90percent.",
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notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held
inconjunction with EvoCOP2017, EvoMusArt2017 and
EvoApplications2017",
- }
Genetic Programming entries for
Uriel Lopez
Leonardo Trujillo
Yuliana Martinez
Pierrick Legrand
Enrique Naredo
Sara Silva
Citations