Learning Ensemble of Decision Trees through Multifactorial Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Yu-Wei Wen and Chuan-Kang Ting",
  title =        "Learning Ensemble of Decision Trees through
                 Multifactorial Genetic Programming",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "5293--5300",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming,
                 Multifactorial evolution, ensemble learning, decision
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7748363",
  abstract =     "Genetic programming (GP) has received considerable
                 successes in machine learning tasks such as prediction
                 and classification. Ensemble learning enables the
                 collaboration of multiple classifiers and effectively
                 improves the classification accuracy. Learning an
                 ensemble of classifiers with GP can simply be achieved
                 by repeated runs of GP; however, the computational cost
                 will be multiplied as well. Recently, multifactorial
                 evolution was proposed to concurrently solve multiple
                 problems with a single population. This study uses the
                 multifactorial evolution and designs a multifactorial
                 genetic programming (MFGP) for efficiently learning an
                 ensemble of decision trees. In the MFGP, each task is
                 associated with one run of GP. The multifactorial
                 evolution enables MFGP to evolve multiple GP
                 classifiers for an ensemble in a single run, which
                 saves a substantial amount of computational cost at
                 repeated runs of GP. The experimental results show that
                 MFGP can learn an ensemble with comparable accuracy,
                 precision, and recall to conventional ensemble learning
                 methods, whereas MFGP requires much less computational
                 resource. The satisfactory outcomes validate the
                 advantages of MFGP in ensemble learning.",
  notes =        "WCCI2016",

Genetic Programming entries for Yu-Wei Wen Chuan-Kang Ting