Automated machine learning: A biologically inspired approach

Zutty, Jason Paul

Title:

Automated machine learning: A biologically inspired approach

dc.contributor.advisor	Lanterman, Aaron D.
dc.contributor.advisor	Rohling, Gregory
dc.contributor.author	Zutty, Jason Paul
dc.contributor.committeeMember	Michaels, Jennifer
dc.contributor.committeeMember	Romberg, Justin
dc.contributor.committeeMember	Davenport, Mark
dc.contributor.committeeMember	Wang, May
dc.contributor.department	Electrical and Computer Engineering
dc.date.accessioned	2019-01-16T17:23:52Z
dc.date.available	2019-01-16T17:23:52Z
dc.date.created	2018-12
dc.date.issued	2018-11-08
dc.date.submitted	December 2018
dc.date.updated	2019-01-16T17:23:52Z
dc.description.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.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/60768
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Machine learning
dc.subject	AutoML
dc.subject	Genetic programing
dc.subject	Automated algorithm design
dc.title	Automated machine learning: A biologically inspired approach
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	Lanterman, Aaron D.
local.contributor.corporatename	School of Electrical and Computer Engineering
local.contributor.corporatename	College of Engineering
relation.isAdvisorOfPublication	8a33b73f-88b1-4907-b2f6-307f5ad37738
relation.isOrgUnitOfPublication	5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication	7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level	Doctoral