Sparse learning and hybrid probabilistic oversampling for Alzheimers Disease diagnosis
- Peng Cao
- Xiaoli Liu
- Danzhe Zhao
- Osmar R. Zaiane, University of Alberta (Database)
Alzheimers Disease (AD) is the most common neurodegenerative disorder associated with aging. Early diagnosis of AD is key to the development, assessment, and monitoring of new treatments for AD. Machine learning approaches are increasingly being applied on the diagnosis of AD from structural MRI. However, the high feature-dimension and imbalanced data learning problem is two major challenges in the study of computer aided AD diagnosis. To circumvent this problem, we propose a novel formulation with hinge loss and sparse group lasso to select the discriminative features since features exhibit certain intrinsic group structures, then we propose a hybrid probabilistic oversampling to alleviate the class imbalanced distribution. Extensive experiments were conducted to compare this method against the baseline and the state-of-the-art methods, and the results illustrated that this proposed method is more effective for diagnosis of AD compared to commonly used techniques.
Citation
P. Cao, X. Liu, D. Zhao, O. Zaiane. "Sparse learning and hybrid probabilistic oversampling for Alzheimers Disease diagnosis". International Conference on Hybrid Intelligent Systems, Marrakech, Morocco, November 2016.| Keywords: | Alzheimer’s disease, Group lasso, classification, imbalanced data |
| Category: | In Conference |
| Web Links: | Webdocs |
BibTeX
@incollection{Cao+al:16,
author = {Peng Cao and Xiaoli Liu and Danzhe Zhao and Osmar R. Zaiane},
title = {Sparse learning and hybrid probabilistic oversampling for Alzheimers
Disease diagnosis},
booktitle = {International Conference on Hybrid Intelligent Systems},
year = 2016,
}Last Updated: November 05, 2019Submitted by Sabina P
