Ontology highlight
ABSTRACT:
SUBMITTER: Ramchandran M
PROVIDER: S-EPMC6980320 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Ramchandran Maya M Patil Prasad P Parmigiani Giovanni G
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 20200101
Multi-study learning uses multiple training studies, separately trains classifiers on each, and forms an ensemble with weights rewarding members with better cross-study prediction ability. This article considers novel weighting approaches for constructing tree-based ensemble learners in this setting. Using Random Forests as a single-study learner, we compare weighting each forest to form the ensemble, to extracting the individual trees trained by each Random Forest and weighting them directly. W ...[more]