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Pareto Front Hyperparameter Selection for Small Metabolomics 2x2 Crossover Designs

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dc.contributor.advisor Ali, Ayesha
dc.contributor.advisor Deeth, Lorna
dc.contributor.author Reavie, Glen
dc.date.accessioned 2021-05-19T12:24:59Z
dc.date.available 2021-05-19T12:24:59Z
dc.date.copyright 2021-05
dc.date.created 2021-04-27
dc.identifier.uri https://hdl.handle.net/10214/25753
dc.description.abstract Using a recently developed stability estimator, stability is leveraged at the cost of discriminatory power, in order to improve feature selection for small 2 x 2 metabolomics crossover designs. This is done using Pareto Front Cross-Validation (PFCV) adapted with an automated hyperparameter selection criteria. PFCV is evaluated for Partial Least Squares Discriminant Analysis’s (PLSDA) Variable Importance Projections, Significant Multivariate Correlations, Nearest Shrunken Centroids and the Soft-Threshold PLSDA using a simulation study and real metabolomics data. Variable importance projections with PFCV provided the best overall feature selection and is recommended for subject sizes as small as 6. However, for larger subject sizes, this recommendation was shown to potentially vary depending on the goals of the practitioner. Overall, the use of PFCV for model selection in small 2 x 2 metabolomics crossover designs is advocated in future research. en_US
dc.description.sponsorship NSERC, Morris Animal Foundation en_US
dc.language.iso en en_US
dc.publisher University of Guelph en_US
dc.rights Attribution-NonCommercial 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/ *
dc.subject crossover designs en_US
dc.subject feature selection stability en_US
dc.subject partial least squares en_US
dc.subject topsis en_US
dc.subject pareto front en_US
dc.subject hyperparameter selection en_US
dc.subject metabolomics en_US
dc.title Pareto Front Hyperparameter Selection for Small Metabolomics 2x2 Crossover Designs en_US
dc.type Thesis en_US
dc.degree.programme Mathematics and Statistics en_US
dc.degree.name Master of Science en_US
dc.degree.department Department of Mathematics and Statistics en_US
dc.degree.grantor University of Guelph en_US


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