Unlabeled Multi-Target Regression with Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Lopez:2020:GECCO,
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author = "Uriel Lopez and Leonardo Trujillo and Sara Silva and
Leonardo Vanneschi and Pierrick Legrand",
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title = "Unlabeled Multi-Target Regression with Genetic
Programming",
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year = "2020",
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editor = "Carlos Artemio {Coello Coello} and
Arturo Hernandez Aguirre and Josu Ceberio Uribe and
Mario Garza Fabre and Gregorio {Toscano Pulido} and
Katya Rodriguez-Vazquez and Elizabeth Wanner and
Nadarajen Veerapen and Efren Mezura Montes and
Richard Allmendinger and Hugo Terashima Marin and
Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and
Heike Trautmann and Ke Tang and John Koza and
Erik Goodman and William B. Langdon and Miguel Nicolau and
Christine Zarges and Vanessa Volz and Tea Tusar and
Boris Naujoks and Peter A. N. Bosman and
Darrell Whitley and Christine Solnon and Marde Helbig and
Stephane Doncieux and Dennis G. Wilson and
Francisco {Fernandez de Vega} and Luis Paquete and
Francisco Chicano and Bing Xue and Jaume Bacardit and
Sanaz Mostaghim and Jonathan Fieldsend and
Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and
Carlos Segura and Carlos Cotta and Michael Emmerich and
Mengjie Zhang and Robin Purshouse and Tapabrata Ray and
Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and
Frank Neumann",
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isbn13 = "9781450371285",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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URL = "https://doi.org/10.1145/3377930.3389846",
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DOI = "doi:10.1145/3377930.3389846",
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booktitle = "Proceedings of the 2020 Genetic and Evolutionary
Computation Conference",
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pages = "976--984",
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size = "9 pages",
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keywords = "genetic algorithms, genetic programming, unlabeled
multi-target regression, multi-target regression,
clustering, RANSAC",
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address = "internet",
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series = "GECCO '20",
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month = jul # " 8-12",
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organisation = "SIGEVO",
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abstract = "Machine Learning (ML) has now become an important and
ubiquitous tool in science and engineering, with
successful applications in many real-world domains.
However, there are still areas in need of improvement,
and problems that are still considered difficult with
off-the-shelf methods. One such problem is Multi Target
Regression (MTR), where the target variable is a
multidimensional tuple instead of a scalar value. In
this work, we propose a more difficult variant of this
problem which we call Unlabeled MTR (uMTR), where the
structure of the target space is not given as part of
the training data. This version of the problem lies at
the intersection of MTR and clustering, an unexplored
problem type. Moreover, this work proposes a solution
method for uMTR, a hybrid algorithm based on Genetic
Programming and RANdom SAmple Consensus (RANSAC). Using
a set of benchmark problems, we are able to show that
this approach can effectively solve the uMTR problem.",
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notes = "Also known as \cite{10.1145/3377930.3389846}
GECCO-2020 A Recombination of the 29th International
Conference on Genetic Algorithms (ICGA) and the 25th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Uriel Lopez
Leonardo Trujillo
Sara Silva
Leonardo Vanneschi
Pierrick Legrand
Citations