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dc.contributor.authorHetland, Hanne Brit
dc.date.accessioned2015-08-05T09:04:41Z
dc.date.available2015-08-05T09:04:41Z
dc.date.copyright2015
dc.date.issued2015-08-05
dc.identifier.urihttp://hdl.handle.net/11250/294716
dc.description.abstractHierarchically Ordered Taxonomic Partial Least Squares (Hot PLS) is a method for classifying data in a hierarchical structure. Since Hot PLS is a relatively new method, we want to study strengths and weaknesses of this. This was done by simulated data with known parameters by using the R package, Simrel. The simulated data was then classified by Hot PLS. Classification error was used as the measure on how good the a method is to classify the data. For finding out which effect the different simulated parameters had on the classification error an ANOVA model was made, where the classification error was the response and the simulatated parameters and methods was the treatments. The simulated data were also classifies by other classifiers PLS, LDA, QDA and KNN, so one could check if the Hot PLS did perform better than the other classifiers. First the Hot PLS was only compared with PLS. The results from these analysis show us that the Hot PLS is a good method for classifying data which has a hierarchical structure.nb_NO
dc.language.isoengnb_NO
dc.publisherNorwegian University of Life Sciences, Ås
dc.rightsNavngivelse 3.0 Norge*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/no/*
dc.subjectHotPLSnb_NO
dc.subjectClassificationnb_NO
dc.subjectHierarchicalnb_NO
dc.titleA simulation study : hierarchical PLS for multi-group classificationnb_NO
dc.title.alternativeEn simuleringsstudie: Hierarkisk PLS for multi-gruppe klassifiseringnb_NO
dc.typeMaster thesisnb_NO
dc.subject.nsiVDP::Mathematics and natural science: 400::Mathematics: 410::Statistics: 412nb_NO
dc.source.pagenumber93nb_NO
dc.description.localcodeM-BIASnb_NO


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