An Empirical Boosting Scheme for ROC-based Genetic Programming Classifiers

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

  author =       "Denis Robilliard and Virginie Marion-Poty and 
                 S\'ebastien Mahler and Cyril Fonlupt",
  title =        "An Empirical Boosting Scheme for ROC-based Genetic
                 Programming Classifiers",
  editor =       "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and 
                 Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
  booktitle =    "Proceedings of the 10th European Conference on Genetic
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "4445",
  year =         "2007",
  address =      "Valencia, Spain",
  month =        "11-13 " # apr,
  pages =        "12--22",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-71602-5",
  isbn13 =       "978-3-540-71602-0",
  DOI =          "doi:10.1007/978-3-540-71605-1_2",
  abstract =     "The so-called ``boosting'' principle was introduced by
                 Schapire and Freund in the 1990s in relation to weak
                 learners in the Probably Approximately Correct
                 computational learning framework. Another practice that
                 has developed in recent years consists in assessing the
                 quality of evolutionary or genetic classifiers with
                 Receiver Operating Characteristics (ROC) curves.
                 Following the RankBoost algorithm by Freund et al.,
                 this article is a cross-bridge between these two
                 techniques, and deals about boosting ROC-based genetic
                 programming classifiers. Updating the weights after a
                 boosting round turns to be the algorithm keystone since
                 the ROC curve does not allow to know directly which
                 training cases are learned or misclassified. We propose
                 a geometrical interpretation of the ROC curve to
                 attribute an error measure to every training case. We
                 validate our ROCboost algorithm on several benchmarks
                 from the UCI-Irvine repository, and we compare boosted
                 Genetic Programming performance with published results
                 on ROC-based Evolution Strategies and Support Vector
  notes =        "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
                 conjunction with EvoCOP2007, EvoBIO2007 and

Genetic Programming entries for Denis Robilliard Virginie Marion-Poty Sebastien Mahler Cyril Fonlupt