Induction of Decision Trees Using Genetic Programming for the Development of SAR Toxicity Models

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

@InProceedings{wang:2005:UKCI,
  author =       "Xue Zhong Wang and Frances V. Buontempo and 
                 Mulaisho Mwense and Anita Young and Daniel Osborn",
  title =        "Induction of Decision Trees Using Genetic Programming
                 for the Development of SAR Toxicity Models",
  booktitle =    "The 5th annual UK Workshop on Computational
                 Intelligence",
  year =         "2005",
  editor =       "Boris Mirkin and George Magoulas",
  pages =        "169--175",
  address =      "London",
  month =        "5-7 " # sep,
  organisation = "Birkbeck College, London Knowledge Lab",
  keywords =     "genetic algorithms, genetic programming, QSAR,
                 EPTree",
  URL =          "http://www.dcs.bbk.ac.uk/ukci05/ukci05proceedings.pdf",
  size =         "7 pages",
  abstract =     "Automatic induction of decision tress and production
                 rules from data to develop structure-activity
                 relationship (SAR) models for toxicity prediction of
                 chemicals has recently received much attention and the
                 majority of methodologies reported in the literature
                 are based upon recursive partitioning employing greedy
                 searches to choose the best splitting attribute and
                 value at each node. These approaches can be successful
                 however the greedy search will necessarily miss regions
                 of the search space. Recent literature has demonstrated
                 the applicability of genetic programming to decision
                 tree induction to overcome this problem. This paper
                 presents a variant of this novel approach, using fewer
                 mutation options and a simpler fitness function,
                 demonstrating its utility in inducing decision trees
                 for ecotoxicity data, via a case study of two datasets
                 giving improved accuracy and generalisation ability
                 over a popular decision tree inducer.",
  notes =        "UKCI 2005 http://www.dcs.bbk.ac.uk/ukci05/

                 vibrio fischeri -- 75 compounds LC50 1093
                 descriptors

                 cholorella vulgaris -- EC50 80 organic
                 compounds

                 University of leeds, AstraZeneca Brixham. Monk's Wood
                 Huntingdon",
}

Genetic Programming entries for Xue Zhong Wang Frances V Buontempo Mulaisho Mwense Anita Young Daniel Osborn

Citations