Date of Award
2017
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Mathematics
First Advisor
Josh Bongard
Abstract
Building and discovering useful features when constructing machine learning models is the central task for the machine learning practitioner. Good features are useful not only in increasing the predictive power of a model but also in illuminating the underlying drivers of a target variable. In this research we propose a novel feature learning technique in which Symbolic regression is endowed with a ``Range Terminal'' that allows it to explore functions of the aggregate of variables over time. We test the Range Terminal on a synthetic data set and a real world data in which we predict seasonal greenness using satellite derived temperature and snow data over a portion of the Arctic. On the synthetic data set we find Symbolic regression with the Range Terminal outperforms standard Symbolic regression and Lasso regression. On the Arctic data set we find it outperforms standard Symbolic regression, fails to beat the Lasso regression, but finds useful features describing the interaction between Land Surface Temperature, Snow, and seasonal vegetative growth in the Arctic.
Language
en
Number of Pages
28 p.
Recommended Citation
Fusting, Christopher Winter, "Temporal Feature Selection with Symbolic Regression" (2017). Graduate College Dissertations and Theses. 806.
https://scholarworks.uvm.edu/graddis/806