Soft-split sparse regression based random forest for predicting future clinical scores of Alzheimer’s disease

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.