ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2020
Tree-Weighting for Multi-Study Ensemble Learners.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Hierarchical resampling for bagging in multistudy prediction with applications to human neurochemical sensing.The annals of applied statistics · 2022Article
- Robustifying genomic classifiers to batch effects via ensemble learning.Bioinformatics (Oxford, England) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
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. We find that incorporating multiple layers of ensembling in the training process by weighting trees increases the robustness of the resulting predictor. Furthermore, we explore how ensembling weights correspond to tree structure, to shed light on the features that determine whether weighting trees directly is advantageous. Finally, we apply our approach to genomic datasets and show that weighting trees improves upon the basic multi-study learning paradigm. Code and supplementary material are available at https://github.com/m-ramchandran/tree-weighting.
Indexed as
Identifiers
31797618PMC6980320What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.