A greedy stacking algorithm for model ensembling and domain weighting.

ABSTRACT: OBJECTIVE:Because it is impossible to know which statistical learning algorithm performs best on a prediction task, it is common to use stacking methods to ensemble individual learners into a more powerful single learner. Stacking algorithms are usually based on linear models, which may run into problems, especially when predictions are highly correlated. In this study, we develop a greedy algorithm for model stacking that overcomes this issue while still being very fast and easy to interpret. We evaluate our greedy algorithm on 7 different data sets from various biomedical disciplines and compare it to linear stacking, genetic algorithm stacking and a brute force approach in different prediction settings. We further apply this algorithm on a task to optimize the weighting of the single do

SUBMITTER: Kurz CF 

PROVIDER: S-EPMC7017540 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature