Date of Award:
5-2013
Document Type:
Dissertation
Degree Name:
Doctor of Philosophy (PhD)
Department:
Mathematics and Statistics
Advisor/Chair:
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
916601548f43bb503100ddf388e6b8fa
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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