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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title: Automated Machine Learning: A Biologically Inspired Approach
Committee:
Dr. Aaron Lanterman, ECE, Chair , Advisor
Dr. Justin Romberg, ECE
Dr. Jennifer Michaels, ECE
Dr. Mark Davenport, ECE
Dr. May Wang, BME
Dr. Greg Rohling, GTRI, Co-Advisor
Abstract:
Machine learning is a robust process by which a computer can discover characteristics of underlying data that enable it to create a model for making future predictions or classifications from new data. Designing machine learning pipelines, unfortunately, is often as much an art as it is a science, requiring pairing of feature construction, feature selection, and learning methods, all with their own sets of parameters. No general machine learning pipeline solution exists; each dataset has unique characteristics that make a particular set of methods and parameters better suited to solving the problem than others. To respond to the challenge of machine learning pipeline design, the field of automated machine learning (autoML) has recently emerged. AutoML seeks to automate the often arduous work of a data scientist, so they can focus on the underlying meanings of the data and spend less time on the tedium of pipeline design and tuning. This dissertation adapts and applies genetic programming to the newly emergent field of automated machine learning. Genetic programming enables the artificial evolution of an algorithm through a nearly infinite search space that otherwise requires a randomized search. This dissertation shows that through the process of genetic programming, it is possible to produce machine learning pipelines, and the evolved pipelines can outperform those created by human researchers.
