Improving Induction of Linear Classification Trees with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Bot:2000:GECCO,
  author =       "Martijn C. J. Bot",
  title =        "Improving Induction of Linear Classification Trees
                 with Genetic Programming",
  pages =        "403--410",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and 
                 Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GP185.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/bot.gecco2000.19jan.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/316984.html",
  abstract =     "Decision trees are a well known technique in machine
                 learning for describing the underlying structure of a
                 dataset. In [Bot and Langdon, 2000] a new
                 representation of decision trees using strong typing in
                 GP was introduced. In the function nodes, a linear
                 combination of variables is made. The effects of
                 techniques such as limited error fitness, fitness
                 sharing Pareto scoring and domination Pareto scoring
                 are evaluated on a set of benchmark classification
                 problems. Comparisons with current state-of-the-art
                 algorithms in machine learning are presented and areas
                 of future research are identified. Results indicate
                 that GP can be applied successfully to classification
                 problems. Limited error fitness reduces runtime while
                 maintaing equal accuracy. Pareto scoring works well
                 against bloat. Fitness sharing Pareto works better than
                 domination Pareto.",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 \cite{whitley:2000:GECCO}",
}

Genetic Programming entries for Martijn C J Bot

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