Semi-supervised genetic programming for classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Filipe {de Lima Arcanjo} and Gisele Lobo Pappa and 
                 Paulo Viana Bicalho and {Wagner Meira, Jr.} and 
                 Altigran Soares {da Silva}",
  title =        "Semi-supervised genetic programming for
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 on Genetic and evolutionary computation",
  year =         "2011",
  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",
  isbn13 =       "978-1-4503-0557-0",
  pages =        "1259--1266",
  keywords =     "genetic algorithms, genetic programming, Genetics
                 based machine learning",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001746",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  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.",
  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