Year of Award
Doctor of Philosophy (PhD)
Department or School/College
Department of Mathematical Sciences
Dave Patterson, Jon Graham, Solomon Harrar, Jesse Johnson
bagging, tree ensemble
University of Montana
Tree ensembles have proven to be a popular and powerful tool for predictive modeling tasks. The theory behind several of these methods (e.g. boosting) has received considerable attention. However, other tree ensemble techniques (e.g. bagging, random forests) have attracted limited theoretical treatment. Specifically, it has remained somewhat unclear as to why the simple act of randomizing the tree growing algorithm should lead to such dramatic improvements in performance. It has been suggested that a specific type of tree ensemble acts by forming a locally adaptive distance metric [Lin and Jeon, 2006]. We generalize this claim to include all tree ensembles methods and argue that this insight can help to explain the exceptional performance of tree ensemble methods. Finally, we illustrate the use of tree ensemble methods for an ecological niche modeling example involving the presence of malaria vectors in Africa.
Elias, Joran, "Randomness In Tree Ensemble Methods" (2009). Graduate Student Theses, Dissertations, & Professional Papers. 795.
© Copyright 2009 Joran Elias