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dc.contributor.advisorNgu, Anne H.H.
dc.contributor.authorPhillips, Clark Raymond ( )
dc.date.accessioned2015-06-26T18:02:51Z
dc.date.available2015-06-26T18:02:51Z
dc.date.issued2015-05
dc.identifier.citationPhillips, C. R. (2015). Employing an efficient and scalable implementation of the Cost Sensitive Alternating Decision Tree algorithm to efficiently link person records (Unpublished thesis). Texas State University, San Marcos, Texas.
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/5576
dc.description.abstractWhen collecting person records for census, identifying individuals accurately is paramount. Over time, people change their phone numbers, their addresses, even their names. Without a universal identifier such as a social security number or a finger-print, it is difficult to know whether two distinct person records represent the same individual. The Cost Sensitive Alternating Decision Tree (CSADT) algorithm (a supervised learning algorithm) is employed as a Record Linkage solution to the problem of resolving whether two person records are the same individual. A person record consists of several attributes such as a name, a phone number, an address, etc. The number of person-record-pairs grows exponentially as the number of records increase. In order to accommodate this exponential growth, a scalable implementation of the CSADT algorithm was employed. A thorough investigation and evaluation are presented demonstrating the effectiveness of this implementation of the CSADT algorithm on linking person records.
dc.formatText
dc.format.extent99 pages
dc.format.medium1 file (.pdf)
dc.language.isoen_US
dc.subjectDecision trees
dc.subjectMachine learning
dc.subjectAlternating decision tree
dc.subject.lcshComputer science--Mathematicsen_US
dc.subject.lcshCombinatorial analysisen_US
dc.titleEmploying an Efficient and Scalable Implementation of the Cost Sensitive Alternating Decision Tree algorithm to Efficiently Link Person Records
txstate.documenttypeThesis
dc.contributor.committeeMemberGao, Byron J.
dc.contributor.committeeMemberLu, Yijuan
thesis.degree.departmentComputer Scienceen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorTexas State Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Scienceen_US
txstate.departmentComputer Science


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