AUC analysis of the pareto-front using multi-objective GP for classification with unbalanced data

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Urvesh Bhowan and Mengjie Zhang and Mark Johnston",
  title =        "AUC analysis of the pareto-front using multi-objective
                 GP for classification with unbalanced data",
  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 =        "845--852",
  keywords =     "genetic algorithms, genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830639",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Learning algorithms can suffer a performance bias when
                 data sets are unbalanced. This paper proposes a
                 Multi-Objective Genetic Programming (MOGP) approach
                 using the accuracy of the minority and majority class
                 as learning objectives. We focus our analysis on the
                 classification ability of evolved Pareto-front
                 solutions using the Area Under the ROC Curve (AUC) and
                 investigate which regions of the objective trade-off
                 surface favour high-scoring AUC solutions. We show that
                 a diverse set of well-performing classifiers is
                 simultaneously evolved along the Pareto-front using the
                 MOGP approach compared to canonical GP where only one
                 solution is found along the objective trade-off
                 surface, and that in some problems the MOGP solutions
                 had better AUC than solutions evolved with canonical GP
                 using hand-crafted fitness functions.",
  notes =        "Also known as \cite{1830639} 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 Urvesh Bhowan Mengjie Zhang Mark Johnston