A Combined Feature Screening Approach of Random Forest and Filter-based Methods for Ultra-high Dimensional Data

Background: Various feature (variable) screening approaches have been proposed in the past decade to mitigate the impact of ultra-high dimensionality in classification and regression problems, including filter based methods such as sure independence screening, and wrapper based methods such as random forest. However, the former type of methods rely heavily on strong modelling assumptions while the latter ones requires an adequate sample size to make the data speak for themselves. These requirements can seldom be met in biochemical studies in cases where we have only access to ultra-high dimensional data with a complex structure and a small number of observations. Objective: In this research, we want to investigate the possibility of combining both filter based screening methods and random forest based screening methods in the regression context. Methods: We have combined four state-of-art filter approaches, namely, sure independence screening (SIS), robust rank correlation based screening (RRCS), high dimensional ordinary least squares projection (HOLP) and a model free sure independence screening procedure based on the distance correlation (DCSIS) from the statistical community with a random forest based Boruta screening method from the machine learning community for regression problems. Results: Among all the combined methods, RF-DCSIS performs better than the other methods in terms of screening accuracy and prediction capability on the simulated scenarios and real benchmark datasets. Conclusion: By empirical study from both extensive simulation and real data, we have shown that both filter based screening and random forest based screening have their pros and cons, while a combination of both may lead to a better feature screening result and prediction capability.