Affiliation:
1. School of Mathematics and Statistics,
Central South University, Changsha, China
2. School of Economics and Management, Changsha University, Changsha, China
Abstract
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.
Method:
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.
Result:
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.
Funder
Hunan Provincial Social Science Foundation of China
Scientific Research Fund of Hunan Provincial Education Department
Science and Technology Plan Project of Changsha City
National Social Science Foundation of China
Publisher
Bentham Science Publishers Ltd.
Subject
Computational Mathematics,Genetics,Molecular Biology,Biochemistry
Cited by
7 articles.
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