- 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",
