A Weighted Random Forests Approach to Improve Predictive Performance.

ABSTRACT: Identifying genetic variants associated with complex disease in high-dimensional data is a challenging problem, and complicated etiologies such as gene-gene interactions are often ignored in analyses. The data-mining method Random Forests (RF) can handle high-dimensions; however, in high-dimensional data, RF is not an effective filter for identifying risk factors associated with the disease trait via complex genetic models such as gene-gene interactions without strong marginal components. Here we propose an extension called Weighted Random Forests (wRF), which incorporates tree-level weights to emphasize more accurate trees in prediction and calculation of variable importance. We demonstrate through simulation and application to data from a genetic study of addiction that wRF can outperfor

SUBMITTER: Winham SJ 

PROVIDER: S-EPMC3912194 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature