A parallel genetic programming for single class classification

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{To:2013:GECCOcomp,

• author = "Cuong To and Mohamed Elati",
• title = "A parallel genetic programming for single class classification",
• booktitle = "GECCO '13 Companion: Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion",
• year = "2013",
• editor = "Christian Blum and Enrique Alba and Thomas Bartz-Beielstein and Daniele Loiacono and Francisco Luna and Joern Mehnen and Gabriela Ochoa and Mike Preuss and Emilia Tantar and Leonardo Vanneschi and Kent McClymont and Ed Keedwell and Emma Hart and Kevin Sim and Steven Gustafson and Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Heike Trautmann and Muhammad Iqbal and Kamran Shafi and Ryan Urbanowicz and Stefan Wagner and Michael Affenzeller and David Walker and Richard Everson and Jonathan Fieldsend and Forrest Stonedahl and William Rand and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Gisele L. Pappa and John Woodward and Jerry Swan and Krzysztof Krawiec and Alexandru-Adrian Tantar and Peter A. N. Bosman and Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and David L. Gonzalez-Alvarez and Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and Kenneth Holladay and Tea Tusar and Boris Naujoks",
• isbn13 = "978-1-4503-1964-5",
• keywords = "genetic algorithms, genetic programming",
• pages = "1579--1586",
• month = "6-10 " # jul,
• organisation = "SIGEVO",
• address = "Amsterdam, The Netherlands",
• DOI = "doi:10.1145/2464576.2466811",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "In this paper, we present an algorithm based on genetic programming for single (one) class classification that uses one set containing similar patterns in training process. This type of problem is called single (one) class classification, a novel detection. The proposed algorithm was tested and compared to seven other traditional methods based on two publicly available transcriptomic and proteomic time series datasets and two public breast cancer datasets. The results show that the algorithm could find most similar patterns in the databases with rather low misclassification rates. We also applied parallel genetic programming for this algorithm and it proves that the island model can give better solutions than sequential genetic programming.",
• notes = "Also known as \cite{2466811} Distributed at GECCO-2013.",

}

Genetic Programming entries for Cuong Chieu To Mohamed Elati

Citations