Large Scale Biomedical Data Analysis with Tree-Based Automated Machine Learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Le:2020:GECCOcomp,
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author = "Trang T. Le and Weixuan Fu and Jason H. Moore",
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title = "Large Scale Biomedical Data Analysis with Tree-Based
Automated Machine Learning",
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year = "2020",
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editor = "Richard Allmendinger and Hugo Terashima Marin and
Efren Mezura Montes and Thomas Bartz-Beielstein and
Bogdan Filipic and Ke Tang and David Howard and
Emma Hart and Gusz Eiben and Tome Eftimov and
William {La Cava} and Boris Naujoks and Pietro Oliveto and
Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and
Xiaodong Li and Saul Zapotecas and Qingfu Zhang and
Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and
Hisao Ishibuchi and Jonathan Fieldsend and
Ozgur Akman and Khulood Alyahya and Juergen Branke and
John R. Woodward and Daniel R. Tauritz and Marco Baioletti and
Josu Ceberio Uribe and John McCall and
Alfredo Milani and Stefan Wagner and Michael Affenzeller and
Bradley Alexander and Alexander (Sandy) Brownlee and
Saemundur O. Haraldsson and Markus Wagner and
Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and
Pablo {Valledor Pellicer} and Thomas Stuetzle and
Matthew Johns and Nick Ross and Ed Keedwell and
Herman Mahmoud and David Walker and Anthony Stein and
Masaya Nakata and David Paetzel and Neil Vaughan and
Stephen Smith and Stefano Cagnoni and Robert M. Patton and
Ivanoe {De Falco} and Antonio {Della Cioppa} and
Umberto Scafuri and Ernesto Tarantino and
Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and
Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and
Richard Everson and Handing Wang and Yaochu Jin and
Erik Hemberg and Riyad Alshammari and
Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and
Ponnuthurai Nagaratnam and Roman Senkerik",
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isbn13 = "9781450371278",
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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/10.1145/3377929.3397770",
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DOI = "doi:10.1145/3377929.3397770",
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booktitle = "Proceedings of the 2020 Genetic and Evolutionary
Computation Conference Companion",
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pages = "21--22",
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size = "2 pages",
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keywords = "genetic algorithms, genetic programming, TPOT,
autoML",
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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 = "Tree-based Pipeline Optimization Tool (TPOT) is an
automated machine learning (AutoML) system that
recommends optimal pipeline for supervised learning
problems by scanning data for novel features, selecting
appropriate models and optimizing their parameters.
However, like other AutoML systems, TPOT may reach
computational resource limits when working on big data
such as whole-genome expression data. We develop two
novel features for TPOT, Feature Set Selector and
Template, which leverage domain knowledge, greatly
reduce the computational expense and flexibly extend
TPOT's application to biomedical big data analysis.",
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notes = "Also known as \cite{10.1145/3377929.3397770}
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
Trang T Le
Weixuan Fu
Jason H Moore
Citations