A new ensemble learning method stratified sampling blending optimizes conventional blending and improves prediction performance.

ABSTRACT:

Motivation

Ensemble learning, as a powerful machine learning method, improves overall prediction performance by combining the prediction results of multiple base models. Blending, as a popular ensemble learning method, can train multiple base models, input the resulting prediction results to further train meta model and obtain final prediction results. However, conventional blending divides the training set by simple random sampling, which causes bias and large variance, thus affecting the stability and accuracy of prediction performance. In this study, we propose a new algorithm of stratified sampling blending (ssBlending), which addresses the algorithm instability of conventional blending caused by the random partition of the training set, further improving the prediction accurac

SUBMITTER: Miao N 

PROVIDER: S-EPMC11908643 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature