Bias in random forest variable importance measures

This thesis proposes to employ an alternative random forest method, the variable importance measure of which can be employed to reliably select relevant predictor variables in any data set. The performance of this method is compared to that of the original random forest method in simulation studies, and is illustrated by an application to the prediction of C-to-U edited sites in plant mitochondrial RNA, re-analyzing the data of that were previously analyzed with the original random forest method.