Large Scale Biomedical Data Analysis with Tree-Based Automated Machine Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Le:2020:GECCOcomp,

• author = "Trang T. Le and Weixuan Fu and Jason H. Moore",
• title = "Large Scale Biomedical Data Analysis with Tree-Based Automated Machine Learning",
• year = "2020",
• 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",
• isbn13 = "9781450371278",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• URL = "https://doi.org/10.1145/3377929.3397770",
• DOI = "doi:10.1145/3377929.3397770",
• booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion",
• pages = "21--22",
• size = "2 pages",
• keywords = "genetic algorithms, genetic programming, TPOT, autoML",
• address = "internet",
• series = "GECCO '20",
• month = jul # " 8-12",
• organisation = "SIGEVO",
• 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.",
• 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