Mixing independent classifiers

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{1277278,
  author =       "Jan Drugowitsch and Alwyn M. Barry",
  title =        "Mixing independent classifiers",
  booktitle =    "GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation",
  year =         "2007",
  editor =       "Dirk Thierens and Hans-Georg Beyer and 
                 Josh Bongard and Jurgen Branke and John Andrew Clark and 
                 Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and 
                 Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and 
                 Julian F. Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Riccardo Poli and Kumara Sastry and 
                 Kenneth Owen Stanley and Thomas Stutzle and 
                 Richard A Watson and Ingo Wegener",
  volume =       "2",
  isbn13 =       "978-1-59593-697-4",
  pages =        "1596--1603",
  address =      "London",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1596.pdf",
  DOI =          "doi:10.1145/1276958.1277278",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, information
                 fusion, learning classifier system (LCS), XCS",
  abstract =     "In this study we deal with the mixing problem, which
                 concerns combining the prediction of independently
                 trained local models to form a global prediction. We
                 deal with it from the perspective of Learning
                 Classifier Systems where a set of classifiers provide
                 the local models. Firstly, we formalise the mixing
                 problem and provide both analytical and heuristic
                 approaches to solving it. The analytical approaches are
                 shown to not scale well with the number of local
                 models, but are nevertheless compared to heuristic
                 models in a set of function approximation tasks. These
                 experiments show that we can design heuristics that
                 exceed the performance of the current state-of-the-art
                 Learning Classifier System XCS, and are competitive
                 when compared to analytical solutions. Additionally, we
                 provide an upper bound on the prediction errors for the
                 heuristic mixing approaches.",
  notes =        "GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071",
}

Genetic Programming entries for Jan Drugowitsch Alwyn M Barry

Citations