Abstract
In this study, we propose a novel sparse regression based random forest (RF) to predict future clinical scores of Alzheimer’s disease (AD) with the baseline scores and the MRI features. To avoid the stair-like decision boundary caused by axis-aligned split function in the conventional RF, we present a supervised method to construct the oblique split function by using sparse regression to select the informative features and transform the original features into the target-like features that are more discriminative. Then, we construct the oblique splitting function by applying the principal component analysis (PCA) on the transformed target-like features. Furthermore, to reduce the negative impact of potential missplit induced by the conventional “hard-split”, we further introduce the “soft-split” technique, in which both left and right nodes are visited with certain weights given a test sample. The experiment results show that sparse regression based RF alone can improve the prediction performance of the conventional RF. And further improvement can be achieved when both of the techniques are combined.
Original language | English |
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Title of host publication | Machine Learning in Medical Imaging - 6th International Workshop, MLMI 2015 Held in Conjunction with MICCAI 2015, Proceedings |
Editors | Luping Zhou, Yinghuan Shi, Li Wang, Qian Wang |
Publisher | Springer Verlag |
Pages | 246-254 |
Number of pages | 9 |
ISBN (Print) | 9783319248875 |
DOIs | |
Publication status | Published - 2015 |
Event | 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015 - Munich, Germany Duration: 2015 Oct 5 → 2015 Oct 5 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 9352 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015 |
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Country/Territory | Germany |
City | Munich |
Period | 15/10/5 → 15/10/5 |
Bibliographical note
Publisher Copyright:© Springer International Publishing Switzerland 2015.
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science