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Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
van, Loon, W; de, Vos, F; Fokkema, M; Szabo, B; Koini, M; Schmidt, R; de, Rooij, M.
Analyzing Hierarchical Multi-View MRI Data With StaPLR: An Application to Alzheimer's Disease Classification.
FRONT NEUROSCI-SWITZ. 2022; 16: 830630
Doi: 10.3389/fnins.2022.830630
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- Co-authors Med Uni Graz
- Koini Marisa
- Schmidt Reinhold
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- Abstract:
- Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure. We also introduce a new view importance measure for StaPLR, which allows us to compare the importance of views at any level of the hierarchy. We apply our extended StaPLR algorithm to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which derived MRI measures are most important for classification, and it outperforms elastic net regression in classification performance.
- Find related publications in this database (Keywords)
- multimodal MRI
- machine learning
- stacked generalization
- penalized regression
- feature selection