A Robust Experimental Evaluation of Automated Multi-Label Classification Methods
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{deSa:2020:GECCO,
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author = "Alex G. C. {de Sa} and Cristiano G. Pimenta and
Gisele L. Pappa and Alex A. Freitas",
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title = "A Robust Experimental Evaluation of Automated
Multi-Label Classification Methods",
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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/10.1145/3377930.3390231",
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DOI = "doi:10.1145/3377930.3390231",
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booktitle = "Proceedings of the 2020 Genetic and Evolutionary
Computation Conference",
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pages = "175--183",
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size = "9 pages",
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keywords = "genetic algorithms, genetic programming, search
spaces, automated machine learning (AutoML), search
methods, multi-label classification",
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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 = "Automated Machine Learning (AutoML) has emerged to
deal with the selection and configuration of algorithms
for a given learning task. With the progression of
AutoML, several effective methods were introduced,
especially for traditional classification and
regression problems. Apart from the AutoML success,
several issues remain open. One issue, in particular,
is the lack of ability of AutoML methods to deal with
different types of data. Based on this scenario, this
paper approaches AutoML for multi-label classification
(MLC) problems. In MLC, each example can be
simultaneously associated to several class labels,
unlike the standard classification task, where an
example is associated to just one class label. In this
work, we provide a general comparison of five automated
multi-label classification methods - two evolutionary
methods, one Bayesian optimization method, one random
search and one greedy search - on 14 datasets and three
designed search spaces. Overall, we observe that the
most prominent method is the one based on a canonical
grammar-based genetic programming (GGP) search method,
namely Auto-MEKAGGP. Auto-MEKAGGP presented the best
average results in our comparison and was statistically
better than all the other methods in different search
spaces and evaluated measures, except when compared to
the greedy search method.",
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notes = "Also known as \cite{10.1145/3377930.3390231}
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
Alex G C de Sa
Cristiano Guimaraes Pimenta
Gisele L Pappa
Alex Alves Freitas
Citations