Partially Bayesian Variable Selection in Classification Trees
Noe, Douglas Alan
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https://hdl.handle.net/2142/87407
Description
Title
Partially Bayesian Variable Selection in Classification Trees
Author(s)
Noe, Douglas Alan
Issue Date
2006
Doctoral Committee Chair(s)
He, Xuming
Department of Study
Statistics
Discipline
Statistics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Statistics
Language
eng
Abstract
An algorithm that dynamically incorporates expert opinion in this way has two potential advantages, each improving with the quality of the expert. First, by de-emphasizing certain subsets of variables during the estimation process, unnecessary computational activity can be avoided. Second, by giving an expert's preferred variables priority, we reduce the chance that a spurious variable will appear in the model. Hence, our resulting models are potentially more interpretable and less unstable than those generated by purely data-driven algorithms. We examine these properties in both applied and simulated contexts, and discuss potential extensions of our partially Bayesian algorithm to ensemble trees and other classification and prediction methods.
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