Dimensionality Reduction in the Creation of Classifiers and the Effects of Correlation, Cluster Overlap, and Modelling Assumptions.

dc.contributor.advisorMcNicholas, Dr. Paul
dc.contributor.authorPetrcich, William
dc.date.accessioned2011-08-31T13:25:46Z
dc.date.available2011-09-10T05:00:05Z
dc.date.copyright2011-08
dc.date.created2011-08-22
dc.date.issued2011-08-31
dc.degree.departmentDepartment of Mathematics and Statisticsen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Scienceen_US
dc.degree.programmeMathematics and Statisticsen_US
dc.description.abstractDiscriminant analysis and random forests are used to create models for classification. The number of variables to be tested for inclusion in a model can be large. The goal of this work was to create an efficient and effective selection program. The first method used was based on the work of others. The resulting models were underperforming, so another approach was adopted. Models were built by adding the variable that maximized new-model accuracy. The two programs were used to generate discriminant-analysis and random forest models for three data sets. An existing software package was also used. The second program outperformed the alternatives. For the small number of runs produced in this study, it outperformed the method that inspired this work. The data sets were studied to identify determinants of performance. No definite conclusions were reached, but the results suggest topics for future study.en_US
dc.identifier.urihttp://hdl.handle.net/10214/2933
dc.language.isoenen_US
dc.publisherUniversity of Guelphen_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectfood authenticationen_US
dc.subjectclassificationen_US
dc.subjectvariable selectionen_US
dc.subjectBICen_US
dc.subjectmclusten_US
dc.subjectrandom forestsen_US
dc.subjectclustvarselen_US
dc.titleDimensionality Reduction in the Creation of Classifiers and the Effects of Correlation, Cluster Overlap, and Modelling Assumptions.en_US
dc.typeThesisen_US

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