Imbalanced Classification with TPG Genetic Programming: Impact of Problem Imbalance and Selection Mechanisms

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{sourbier:2022:GECCOcomp,

• author = "Nicolas Sourbier and Justine Bonnot and Olivier Gesny and Frederic Majorczyk and Karol Desnos and Thomas Guyet and Maxime Pelcat",
• title = "Imbalanced Classification with {TPG} Genetic Programming: Impact of Problem Imbalance and Selection Mechanisms",
• booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion",
• year = "2022",
• 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",
• pages = "608--611",
• address = "Boston, USA",
• series = "GECCO '22",
• month = "9-13 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, tangled program graphs, selection, machine learning, classification, imbalanced data",
• isbn13 = "978-1-4503-9268-6/22/07",
• DOI = "doi:10.1145/3520304.3529008",
• 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.",
• 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