ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
- 2 English
- 2 Public
- Ensemble Learning
- 1 Applied mathematics
- 1 Artificial intelligence
- 1 Biostatistics
- 1 Classification
- 1 Computer science
- 1 Functional Data Analysis
- more
- 1 Health care management
- 1 Lupus
- 1 Machine Learning
- 1 Optimizing
- 1 Performance Measure
- 1 Sensitivity
- 1 Specificity
- 1 Statistics
- 1 Supervised Learning
- Dwarf Galaxies as Laboratories of Protogalaxy Physics: Canonical Star Formation Laws at Low Metallicity
- Evolutionary Genetics of CORL Proteins
- Social Skills and Executive Functioning in Children with PCDH-19
- Deep Domain Fusion for Adaptive Image Classification
- Software Defined Pulse-Doppler Radar for Over-The-Air Applications: The Joint Radar-Communications Experiment
Ensemble learning methods like bagging, boosting, adaptive boosting, stacking have traditionally shown promising results in improving the predictive accuracy in classification. These techniques have recently been widely used in various domains and applications owing to the improvements in computational efficiency and distributed computing advances. However, with the advent of wide variety of applications of machine learning techniques to class imbalance problems, further focus is needed to evaluate, improve and optimize other performance measures such as sensitivity (true positive rate) and specificity (true negative rate) in classification. This thesis demonstrates a novel approach to evaluate and optimize the performance measures (specifically …
- Contributors
- Bahl, Neeraj Dharampal, Bansal, Ajay, Amresh, Ashish, et al.
- Created Date
- 2017
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 the temperature domain …
- Contributors
- Buscaglia, Robert, Kamarianakis, Yiannis, Armbruster, Dieter, et al.
- Created Date
- 2018