Skip to main content

Supervised and Ensemble Classification of Multivariate Functional Data: Applications to Lupus Diagnosis

Abstract This dissertation investigates the classification of systemic lupus erythematosus (SLE) in the presence of non-SLE alternatives, while developing novel curve classification methodologies with wide ranging applications. Functional data representations of plasma thermogram measurements and the corresponding derivative curves provide predictors yet to be investigated for SLE identification. Functional nonparametric classifiers form a methodological basis, which is used herein to develop a) the family of ESFuNC segment-wise curve classification algorithms and b) per-pixel ensembles based on logistic regression and fused-LASSO. The proposed methods achieve test set accuracy rates as high as 94.3%, while returning information about regions of ... (more)
Created Date 2018
Contributor Buscaglia, Robert (Author) / Kamarianakis, Yiannis (Advisor) / Armbruster, Dieter (Committee member) / Lanchier, Nicholas (Committee member) / McCulloch, Robert (Committee member) / Reiser, Mark (Committee member) / Arizona State University (Publisher)
Subject Statistics / Biostatistics / Applied mathematics / Classification / Ensemble Learning / Functional Data Analysis / Lupus / Supervised Learning
Type Doctoral Dissertation
Extent 213 pages
Language English
Note Doctoral Dissertation Applied Mathematics 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

  Full Text
5.5 MB application/pdf
Download Count: 512

Description Dissertation/Thesis