Induction of decision trees using genetic programming for the development of SAR toxicity models
Wang, XZ, Frances V. Buontempo, Mulaisho Mwense, Young, A, Daniel Osborn · UCL Discovery (University College London) · 2005
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.