Reuse of program trees in genetic programming with a new fitness function in high-dimensional unbalanced classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Pei:2019:GECCOcomp,
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author = "Wenbin Pei and Bing Xue and Lin Shang and
Mengjie Zhang",
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title = "Reuse of program trees in genetic programming with a
new fitness function in high-dimensional unbalanced
classification",
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booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary
Computation Conference Companion",
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year = "2019",
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editor = "Richard Allmendinger and Carlos Cotta and
Carola Doerr and Pietro S. Oliveto and Thomas Weise and
Ales Zamuda and Anne Auger and Dimo Brockhoff and
Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and
David Camacho-Fernandez and Massimiliano Vasile and
Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and
Saul Zapotecas and Qingfu Zhang and Ozgur Akman and
Khulood Alyahya and Juergen Branke and
Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and
Josu {Ceberio Uribe} and Valentino Santucci and
Marco Baioletti and John McCall and Emma Hart and
Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and
Chika Oshima and Stefan Wagner and
Michael Affenzeller and Eneko Osaba and Javier {Del Ser} and
Pascal Kerschke and Boris Naujoks and Vanessa Volz and
Anna I Esparcia-Alcazar and Riyad Alshammari and
Erik Hemberg and Tokunbo Makanju and Brad Alexander and
Saemundur O. Haraldsson and Markus Wagner and
Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and
Thomas Stuetzle and David Walker and Matt Johns and
Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and
Takato Tatsumi and Nadarajen Veerapen and
Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and
Stephen Smith and Stefano Cagnoni and
Robert M. Patton and William {La Cava} and Randal Olson and
Patryk Orzechowski and Ryan Urbanowicz 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
Marcus Gallagher and Mike Preuss and
Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale",
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isbn13 = "978-1-4503-6748-6",
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pages = "187--188",
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address = "Prague, Czech Republic",
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DOI = "doi:10.1145/3319619.3321958",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "13-17 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming, Class
Imbalance, High-dimensionality",
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size = "2 pages",
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abstract = "Genetic programming (GP) may also evolve biased
classifiers when having the class imbalance issue.
Class imbalance is a difficult but important issue, and
high-dimensionality brings difficulty when addressing
the class imbalance issue. This paper focuses on
addressing the performance bias of GP in classification
with high-dimensional unbalanced data, with the goal of
increasing the accuracies of the majority class and the
minority class, as well as saving the training time. In
this paper, a new fitness function is developed to
address the class unbalanced issue, and moreover, a
strategy is proposed to reuse previous good GP trees
when using multiple GP processes to build a
multi-classifier system. Experimental results show the
better performance of the proposed method.",
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notes = "Also known as \cite{3321958} GECCO-2019 A
Recombination of the 28th International Conference on
Genetic Algorithms (ICGA) and the 24th Annual Genetic
Programming Conference (GP)",
- }
Genetic Programming entries for
Wenbin Pei
Bing Xue
Lin Shang
Mengjie Zhang
Citations