An introspective comparison of random forest-based classifiers for the analysis of cluster-correlated data by way of RF++.

ABSTRACT: Many mass spectrometry-based studies, as well as other biological experiments produce cluster-correlated data. Failure to account for correlation among observations may result in a classification algorithm overfitting the training data and producing overoptimistic estimated error rates and may make subsequent classifications unreliable. Current common practice for dealing with replicated data is to average each subject replicate sample set, reducing the dataset size and incurring loss of information. In this manuscript we compare three approaches to dealing with cluster-correlated data: unmodified Breiman's Random Forest (URF), forest grown using subject-level averages (SLA), and RF++ with subject-level bootstrapping (SLB). RF++, a novel Random Forest-based algorithm implemented in C++, ha

SUBMITTER: Karpievitch YV 

PROVIDER: S-EPMC2739274 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature