Machine learning of poorly predictable ecological data

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

  author =       "Y. Shan and D. Paull and R. I. McKay",
  title =        "Machine learning of poorly predictable ecological
  journal =      "Ecological Modelling",
  year =         "2006",
  volume =       "195",
  number =       "1-2",
  pages =        "129--138",
  month =        "15 " # may,
  note =         "Selected Papers from the Third Conference of the
                 International Society for Ecological Informatics
                 (ISEI), August 26--30, 2002, Grottaferrata, Rome,
  keywords =     "genetic algorithms, genetic programming, Decision
                 trees, Neural networks, Support vector machines,
                 Southern brown bandicoot, Spatial distribution
  DOI =          "doi:10.1016/j.ecolmodel.2005.11.015",
  abstract =     "a variety of machine learning techniques to a
                 difficult modelling problem, the spatial distribution
                 of an endangered Australian marsupial, the southern
                 brown bandicoot (Isoodon obesulus). Four learning
                 techniques decision trees/rules, neural networks,
                 support vector machines and genetic programming were
                 applied to the problem. Support vector and neural
                 network approaches gave marginally better predictivity,
                 but in the context of low overall accuracy, decision
                 trees and genetic programming gave more useful results
                 because of the human comprehensibility of their

Genetic Programming entries for Yin Shan David Paull R I (Bob) McKay