Semi-supervised genetic programming for classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Arcanjo:2011:GECCO,
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author = "Filipe {de Lima Arcanjo} and Gisele Lobo Pappa and
Paulo Viana Bicalho and {Wagner Meira, Jr.} and
Altigran Soares {da Silva}",
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title = "Semi-supervised genetic programming for
classification",
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booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
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year = "2011",
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editor = "Natalio Krasnogor and Pier Luca Lanzi and
Andries Engelbrecht and David Pelta and Carlos Gershenson and
Giovanni Squillero and Alex Freitas and
Marylyn Ritchie and Mike Preuss and Christian Gagne and
Yew Soon Ong and Guenther Raidl and Marcus Gallager and
Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and
Nikolaus Hansen and Silja Meyer-Nieberg and
Jim Smith and Gus Eiben and Ester Bernado-Mansilla and
Will Browne and Lee Spector and Tina Yu and Jeff Clune and
Greg Hornby and Man-Leung Wong and Pierre Collet and
Steve Gustafson and Jean-Paul Watson and
Moshe Sipper and Simon Poulding and Gabriela Ochoa and
Marc Schoenauer and Carsten Witt and Anne Auger",
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isbn13 = "978-1-4503-0557-0",
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pages = "1259--1266",
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keywords = "genetic algorithms, genetic programming, Genetics
based machine learning",
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month = "12-16 " # jul,
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organisation = "SIGEVO",
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address = "Dublin, Ireland",
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DOI = "doi:10.1145/2001576.2001746",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "Learning from unlabeled data provides innumerable
advantages to a wide range of applications where there
is a huge amount of unlabeled data freely available.
Semi-supervised learning, which builds models from a
small set of labeled examples and a potential large set
of unlabeled examples, is a paradigm that may
effectively use those unlabeled data. Here we propose
KGP, a semi-supervised transductive genetic programming
algorithm for classification. Apart from being one of
the first semi-supervised algorithms, it is
transductive (instead of inductive), i.e., it requires
only a training dataset with labeled and unlabeled
examples, which should represent the complete data
domain. The algorithm relies on the three main
assumptions on which semi-supervised algorithms are
built, and performs both global search on labeled
instances and local search on unlabeled instances.
Periodically, unlabeled examples are moved to the
labeled set after a weighted voting process performed
by a committee. Results on eight UCI datasets were
compared with Self-Training and KNN, and showed KGP as
a promising method for semi-supervised learning.",
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notes = "Also known as \cite{2001746} GECCO-2011 A joint
meeting of the twentieth international conference on
genetic algorithms (ICGA-2011) and the sixteenth annual
genetic programming conference (GP-2011)",
- }
Genetic Programming entries for
Filipe de Lima Arcanjo
Gisele L Pappa
Paulo Viana Bicalho
Wagner Meira
Altigran S da Silva
Citations