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dc.contributor.advisorYoshida, Ruriko
dc.contributor.authorVu, Carolyne
dc.date.accessioned2019-05-15T19:38:28Z
dc.date.available2019-05-15T19:38:28Z
dc.date.issued2019-03
dc.identifier.urihttp://hdl.handle.net/10945/62313
dc.descriptionApproved for public release. distribution is unlimiteden_US
dc.description.abstractThe rise of accessible real-world data creates a growing interest in effective methods for accurate classification, especially for networks with incomplete information. The intelligence community requires an understanding of a network before the team can develop a strategy to combat the adversary. These problems are typically time-sensitive; however, gathering complete and actionable intelligence is a challenging mission. An adversary’s actions are secretive in nature. Crucial information is deliberately concealed. Intentionally dubious information creates problematic noise. Therefore, if an observed incomplete network can be classified as-is without delay, the network can be properly analyzed for a strategy to be devised and acted upon earlier. This thesis considers a machine learning technique for classification of incomplete networks. We examine the effects of training the model with complete and incomplete information. Observed network data and their structural features are classified into technological, social, information, and biological categories using supervised learning methods.en_US
dc.description.urihttp://archive.org/details/amethodforclassi1094562313
dc.publisherMonterey, CA; Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleA METHOD FOR CLASSIFICATION OF INCOMPLETE NETWORKS: TRAINING THE MODEL WITH COMPLETE AND INCOMPLETE INFORMATIONen_US
dc.typeThesisen_US
dc.contributor.secondreaderAlderson, David L., Jr.
dc.contributor.departmentOperations Research (OR)
dc.subject.authornetwork analysisen_US
dc.subject.authorincomplete informationen_US
dc.subject.authornetwork classificationen_US
dc.subject.authorgraph theoryen_US
dc.description.recognitionOutstanding Thesisen_US
dc.description.serviceLieutenant, United States Navyen_US
etd.thesisdegree.nameMaster of Science in Operations Researchen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineOperations Researchen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.identifier.thesisid30399


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