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Multi-study learning uses multiple training studies, separately trains classifiers on individual studies, and then forms ensembles 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 perform a comparison of either weighting each forest to form the ensemble, or extracting the individual trees trained by each Random Forest and weighting them directly. We consider weighting approaches that reward cross-study replicability within the training set. We find that incorporating multiple layers of ensembling in the training process increases the robustness of the resulting predictor. Furthermore, we explore the mechanisms by which the ensembling weights correspond to the internal structure of trees to shed light on the important features in determining the relationship between the Random Forests algorithm and the true outcome model. Finally, we apply our approach to genomic datasets and show that our method improves upon the basic multi-study learning paradigm.
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Conditions on abruptness in a gradient-ascent Maximum Entropy learner
When does a gradual learning rule translate into gradual learning performance? This paper studies a gradient-ascent Maximum Entropy phonotactic learner, as applied to two- alternative forced-choice performance expressed as log-odds. The main result is that slow initial performance cannot accelerate later if the initial weights are near zero, but can if they are not. Stated another way, abrupt- ness in this learner is an effect of transfer, either from Universal Grammar in the form of an initial weighting, or from previous learning in the form of an acquired weighting.
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- Award ID(s):
- 1651105
- PAR ID:
- 10065767
- Date Published:
- Journal Name:
- Proceedings of the Society for Computation in Linguistics
- Volume:
- 1
- Page Range / eLocation ID:
- 113-124
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.7275/R5XG9PBX
