Title:
Assessing the use of voting methods to improve Bayesian network structure learning

Simple item page
dc.contributor.advisor Styczynski, Mark P.
dc.contributor.author Abu-Hakmeh, Khaldoon E. en_US
dc.contributor.committeeMember Grover, Martha
dc.contributor.committeeMember Realff, Matthew J.
dc.contributor.department Chemical Engineering en_US
dc.date.accessioned 2013-01-17T21:33:56Z
dc.date.available 2013-01-17T21:33:56Z
dc.date.issued 2012-08-27 en_US
dc.description.abstract Structure inference in learning Bayesian networks remains an active interest in machine learning due to the breadth of its applications across numerous disciplines. As newer algorithms emerge to better handle the task of inferring network structures from observational data, network and experiment sizes heavily impact the performance of these algorithms. Specifically difficult is the task of accurately learning networks of large size under a limited number of observations, as often encountered in biological experiments. This study evaluates the performance of several leading structure learning algorithms on large networks. The selected algorithms then serve as a committee, which then votes on the final network structure. The result is a more selective final network, containing few false positives, with compromised ability to detect all network features. en_US
dc.description.degree MS en_US
dc.identifier.uri http://hdl.handle.net/1853/45826
dc.publisher Georgia Institute of Technology en_US
dc.subject Metabolomics en_US
dc.subject Bioinformatics en_US
dc.subject Machine learning en_US
dc.subject Bayesian networks en_US
dc.subject.lcsh Artificial intelligence
dc.subject.lcsh Algorithms
dc.subject.lcsh Biology Data processing
dc.title Assessing the use of voting methods to improve Bayesian network structure learning en_US
dc.type Text
dc.type.genre Thesis
dspace.entity.type Publication
local.contributor.advisor Styczynski, Mark P.
local.contributor.corporatename School of Chemical and Biomolecular Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 932cc32a-66dd-4530-afde-796f557fee0b
relation.isOrgUnitOfPublication 6cfa2dc6-c5bf-4f6b-99a2-57105d8f7a6f
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
abuhakmeh_khaldoon_e_201212_mast.pdf
Size:
1.75 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Theses and Dissertations