Variable importance-weighted Random Forests.

ABSTRACT:

Background

Random Forests is a popular classification and regression method that has proven powerful for various prediction problems in biological studies. However, its performance often deteriorates when the number of features increases. To address this limitation, feature elimination Random Forests was proposed that only uses features with the largest variable importance scores. Yet the performance of this method is not satisfying, possibly due to its rigid feature selection, and increased correlations between trees of forest.

Methods

We propose variable importance-weighted Random Forests, which instead of sampling features with equal probability at each node to build up trees, samples features according to their variable importance scores, and then select the best split f

SUBMITTER: Liu Y 

PROVIDER: S-EPMC6051549 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature