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:1.8120

@InProceedings{Pei:2019:GECCOcomp,

• author = "Wenbin Pei and Bing Xue and Lin Shang and Mengjie Zhang",
• title = "Reuse of program trees in genetic programming with a new fitness function in high-dimensional unbalanced classification",
• booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion",
• year = "2019",
• 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",
• isbn13 = "978-1-4503-6748-6",
• pages = "187--188",
• address = "Prague, Czech Republic",
• DOI = "doi:10.1145/3319619.3321958",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• month = "13-17 " # jul,
• organisation = "SIGEVO",
• keywords = "genetic algorithms, genetic programming, Class Imbalance, High-dimensionality",
• size = "2 pages",
• 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.",
• 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