Feature Selection to Improve Generalisation of Genetic Programming for High-Dimensional Symbolic Regression

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

@Article{Chen:2017:ieeeTEC,
  author =       "Qi Chen and Mengjie Zhang and Bing Xue",
  title =        "Feature Selection to Improve Generalisation of Genetic
                 Programming for High-Dimensional Symbolic Regression",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2017",
  volume =       "21",
  number =       "5",
  pages =        "792--806",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Symbolic
                 Regression",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2017.2683489",
  abstract =     "When learning from high-dimensional data for symbolic
                 regression, genetic programming typically could not
                 generalise well. Feature selection, as a data
                 preprocessing method, can potentially contribute not
                 only to improving the efficiency of learning algorithms
                 but also to enhancing the generalisation ability.
                 However, in genetic programming for high-dimensional
                 symbolic regression, feature selection before learning
                 is seldom considered. In this work, we propose a new
                 feature selection method based on permutation to select
                 features for high dimensional symbolic regression using
                 genetic programming. A set of experiments has been
                 conducted to investigate the performance of the
                 proposed method on the generalisation of genetic
                 programming for high-dimensional symbolic regression.
                 The regression results confirm the superior performance
                 of the proposed method over the other examined feature
                 selection methods. Further analysis indicates that the
                 models evolved by the proposed method are more likely
                 to contain only the truly relevant features and have
                 better interpretability.",
  notes =        "also known as \cite{7879832}",
}

Genetic Programming entries for Qi Chen Mengjie Zhang Bing Xue

Citations