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Temporal Feature Selection with Symbolic Regression

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.

Identiferoai:union.ndltd.org:uvm.edu/oai:scholarworks.uvm.edu:graddis-1805
Date01 January 2017
CreatorsFusting, Christopher Winter
PublisherScholarWorks @ UVM
Source SetsUniversity of Vermont
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceGraduate College Dissertations and Theses

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