Imbalanced Classification with TPG Genetic Programming: Impact of Problem Imbalance and Selection Mechanisms
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{sourbier:2022:GECCOcomp,
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author = "Nicolas Sourbier and Justine Bonnot and
Olivier Gesny and Frederic Majorczyk and Karol Desnos and
Thomas Guyet and Maxime Pelcat",
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title = "Imbalanced Classification with {TPG} Genetic
Programming: Impact of Problem Imbalance and Selection
Mechanisms",
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booktitle = "Proceedings of the 2022 Genetic and Evolutionary
Computation Conference Companion",
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year = "2022",
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editor = "Heike Trautmann and Carola Doerr and
Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and
Marcus Gallagher and Yew-Soon Ong and
Abhishek Gupta and Anna V Kononova and Hao Wang and
Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and
Fabio Caraffini and Johann Dreo and Anne Auger and
Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and
Nikolaus Hansen and Olaf Mersmann and Petr Posik and
Tea Tusar and Dimo Brockhoff and Tome Eftimov and
Pascal Kerschke and Boris Naujoks and Mike Preuss and
Vanessa Volz and Bilel Derbel and Ke Li and
Xiaodong Li and Saul Zapotecas and Qingfu Zhang and
Mark Coletti and Catherine (Katie) Schuman and
Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and
Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and
Richard Allmendinger and Jussi Hakanen and
Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and
John McCall and Jaume Bacardit and
Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and
David Walker and Jamal Toutouh and UnaMay O'Reilly and
Penousal Machado and Joao Correia and Sergio Nesmachnow and
Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and
Francisco {Fernandez de Vega} and Giuseppe Paolo and
Alex Coninx and Antoine Cully and Adam Gaier and
Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and
Vesna Nowack and Aymeric Blot and Emily Winter and
William B. Langdon and Justyna Petke and
Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and
Thomas Stuetzle and David Paetzel and
Alexander Wagner and Michael Heider and Nadarajen Veerapen and
Katherine Malan and Arnaud Liefooghe and Sebastien Verel and
Gabriela Ochoa and Mohammad Nabi Omidvar and
Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and
Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and
Jean-Baptiste Mouret and Stephane Doncieux and
Stefanos Nikolaidis and Julian Togelius and
Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and
Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and
Ofer Shir and Lee Spector and Alma Rahat and
Richard Everson and Jonathan Fieldsend and Handing Wang and
Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and
Michael Kommenda and William {La Cava} and
Gabriel Kronberger and Steven Gustafson",
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pages = "608--611",
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address = "Boston, USA",
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series = "GECCO '22",
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month = "9-13 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, tangled
program graphs, selection, machine learning,
classification, imbalanced data",
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isbn13 = "978-1-4503-9268-6/22/07",
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DOI = "doi:10.1145/3520304.3529008",
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abstract = "Recent research advances on Tangled Program Graphs
(TPGs) have demonstrated that Genetic Programming (GP)
can be used to build accurate classifiers. However,
this performance has been tested on balanced
classification problems while most of the real world
classification problems are imbalanced, with both
over-represented classes and rare classes.This paper
explores the effect of imbalanced data on the
performance of a TPG classifier, and proposes
mitigation methods for imbalance-caused classifier
performance degradation using adapted GP selection
phases. The GP selection phase is characterized by a
fitness function, and by a comparison operator. We show
that adapting the TPG to imbalanced data significantly
improves the classifier performance. The proposed
adaptations on the fitness make the TPG agent capable
to fit a model even with 104 less examples than the
majority class whereas the revised selection phase of
the GP process increases the robustness of the method
for moderate imbalance ratios.",
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notes = "GECCO-2022 A Recombination of the 31st International
Conference on Genetic Algorithms (ICGA) and the 27th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Nicolas Sourbier
Justine Bonnot
Olivier Gesny
Frederic Majorczyk
Karol Desnos
Thomas Guyet
Maxime Pelcat
Citations