Boosting Tree-Assisted Multitask Deep Learning for Small Scientific Datasets.

ABSTRACT: Machine learning approaches have had tremendous success in various disciplines. However, such success highly depends on the size and quality of datasets. Scientific datasets are often small and difficult to collect. Currently, improving machine learning performance for small scientific datasets remains a major challenge in many academic fields, such as bioinformatics or medical science. Gradient boosting decision tree (GBDT) is typically optimal for small datasets, while deep learning often performs better for large datasets. This work reports a boosting tree-assisted multitask deep learning (BTAMDL) architecture that integrates GBDT and multitask deep learning (MDL) to achieve near-optimal predictions for small datasets when there exists a large dataset that is well correlated to the smal

SUBMITTER: Jiang J 

PROVIDER: S-EPMC7350172 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature