Date of Award:
5-2013
Document Type:
Dissertation
Degree Name:
Doctor of Philosophy (PhD)
Department:
Mathematics and Statistics
Committee
Adele Cutler
Abstract
This work presents an enhancement to the classification tree algorithm which forms the basis for Random Forests. Differently from the classical tree-based methods that focus on one variable at a time to separate the observations, the new algorithm performs the search for the best split in two-dimensional space using a linear combination of variables. Besides the classification, the method can be used to determine variables interaction and perform feature extraction. Theoretical investigations and numerical simulations were used to analyze the properties and performance of the new approach. Comparison with other popular classification methods was performed using simulated and real data examples. The algorithm was implemented as an extension package for the statistical computing environment R and is available for free download under the GNU General Public License.
Checksum
0549aac85e01712d9f9b808e8c0f5f18
Recommended Citation
Parfionovas, Andrejus, "Enhancement of Random Forests Using Trees with Oblique Splits" (2013). All Graduate Theses and Dissertations. 1508.
https://digitalcommons.usu.edu/etd/1508
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