TY - GEN
T1 - Soft-split sparse regression based random forest for predicting future clinical scores of Alzheimer’s disease
AU - Huang, Lei
AU - Gao, Yaozong
AU - Jin, Yan
AU - Thung, Kim Han
AU - Shen, Dinggang
N1 - Funding Information:
This work was supported by the National Institute of Health grants EB006733, EB008374, EB009634, AG041721, MH100217, and AG042599.
Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84951971797&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-24888-2_30
DO - 10.1007/978-3-319-24888-2_30
M3 - Conference contribution
AN - SCOPUS:84951971797
SN - 9783319248875
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 246
EP - 254
BT - Machine Learning in Medical Imaging - 6th International Workshop, MLMI 2015 Held in Conjunction with MICCAI 2015, Proceedings
A2 - Zhou, Luping
A2 - Shi, Yinghuan
A2 - Wang, Li
A2 - Wang, Qian
PB - Springer Verlag
T2 - 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
Y2 - 5 October 2015 through 5 October 2015
ER -