Prediction of expected performance for a genetic programming classifier

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Martinez:2016:GPEM,
  author =       "Yuliana Martinez and Leonardo Trujillo and 
                 Pierrick Legrand and Edgar Galvan-Lopez",
  title =        "Prediction of expected performance for a genetic
                 programming classifier",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2016",
  volume =       "17",
  number =       "4",
  pages =        "409--449",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, Problem
                 difficulty, Supervised learning",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-016-9265-9",
  size =         "41 pages",
  abstract =     "The estimation of problem difficulty is an open issue
                 in genetic programming (GP). The goal of this work is
                 to generate models that predict the expected
                 performance of a GP-based classifier when it is applied
                 to an unseen task. Classification problems are
                 described using domain-specific features, some of which
                 are proposed in this work, and these features are given
                 as input to the predictive models. These models are
                 referred to as predictors of expected performance. We
                 extend this approach by using an ensemble of
                 specialized predictors (SPEP), dividing classification
                 problems into groups and choosing the corresponding
                 SPEP. The proposed predictors are trained using 2D
                 synthetic classification problems with balanced
                 datasets. The models are then used to predict the
                 performance of the GP classifier on unseen real-world
                 datasets that are multidimensional and imbalanced. This
                 work is the first to provide a performance prediction
                 of a GP system on test data, while previous works
                 focused on predicting training performance. Accurate
                 predictive models are generated by posing a symbolic
                 regression task and solving it with GP. These results
                 are achieved by using highly descriptive features and
                 including a dimensionality reduction stage that
                 simplifies the learning and testing process. The
                 proposed approach could be extended to other
                 classification algorithms and used as the basis of an
                 expert system for algorithm selection.",
}

Genetic Programming entries for Yuliana Martinez Leonardo Trujillo Pierrick Legrand Edgar Galvan Lopez

Citations