Genetic rule extraction optimizing brier score

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Ulf Johansson and Rikard Konig and Lars Niklasson",
  title =        "Genetic rule extraction optimizing brier score",
  booktitle =    "GECCO '10: Proceedings of the 12th annual conference
                 on Genetic and evolutionary computation",
  year =         "2010",
  editor =       "Juergen Branke and Martin Pelikan and Enrique Alba and 
                 Dirk V. Arnold and Josh Bongard and 
                 Anthony Brabazon and Juergen Branke and Martin V. Butz and 
                 Jeff Clune and Myra Cohen and Kalyanmoy Deb and 
                 Andries P Engelbrecht and Natalio Krasnogor and 
                 Julian F. Miller and Michael O'Neill and Kumara Sastry and 
                 Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and 
                 Carsten Witt",
  isbn13 =       "978-1-4503-0072-8",
  pages =        "1007--1014",
  keywords =     "genetic algorithms, genetic programming, Genetics
                 based machine learning",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830668",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Most highly accurate predictive modelling techniques
                 produce opaque models. When comprehensible models are
                 required, rule extraction is sometimes used to generate
                 a transparent model, based on the opaque. Naturally,
                 the extracted model should be as similar as possible to
                 the opaque. This criterion, called fidelity, is
                 therefore a key part of the optimisation function in
                 most rule extracting algorithms. To the best of our
                 knowledge, all existing rule extraction algorithms
                 targeting fidelity use 0/1 fidelity, i.e., maximise the
                 number of identical classifications. In this paper, we
                 suggests and evaluate a rule extraction algorithm using
                 a more informed fidelity criterion. More specifically,
                 the novel algorithms, which is based on genetic
                 programming, minimises the difference in probability
                 estimates between the extracted and the opaque models,
                 by using the generalised Brier score as fitness
                 function. Experimental results from 26 UCI data sets
                 show that the suggested algorithm obtained considerably
                 higher accuracy and significantly better AUC than both
                 the exact same rule extraction algorithm maximizing 0/1
                 fidelity, and the standard tree inducer J48. Somewhat
                 surprisingly, rule extraction using the more informed
                 fidelity metric normally resulted in less complex
                 models, making sure that the improved predictive
                 performance was not achieved on the expense of
  notes =        "Also known as \cite{1830668} GECCO-2010 A joint
                 meeting of the nineteenth international conference on
                 genetic algorithms (ICGA-2010) and the fifteenth annual
                 genetic programming conference (GP-2010)",

Genetic Programming entries for Ulf Johansson Rikard Konig Lars Niklasson