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This paper proposes FREEtree, a tree-based method for high dimensionallongitudinal data with correlated features. Popular machine learningapproaches, like Random Forests, commonly used for variable selection do notperform well when there are correlated features and do not account for dataobserved over time. FREEtree deals with longitudinal data by using a piecewiserandom effects model. It also exploits the network structure of the features byfirst clustering them using weighted correlation network analysis, namelyWGCNA. It then conducts a screening step within each cluster of features and aselection step among the surviving features, that provides a relativelyunbiased way to select features. By using dominant principle components asregression variables at each leaf and the original features as splittingvariables at splitting nodes, FREEtree maintains its interpretability andimproves its computational efficiency. The simulation results show thatFREEtree outperforms other tree-based methods in terms of prediction accuracy,feature selection accuracy, as well as the ability to recover the underlyingstructure.

FREEtree: A Tree-based Approach for High Dimensional Longitudinal Data With Correlated Features