  Automating machine learning has achieved remarkable technological
developments in recent years, and building an automated machine learning
pipeline is now an essential task. The model ensemble is the technique of
combining multiple models to get a better and more robust model. However,
existing automated machine learning tends to be simplistic in handling the
model ensemble, where the ensemble strategy is fixed, such as stacked
generalization. There have been many techniques on different ensemble methods,
especially ensemble selection, and the fixed ensemble strategy limits the upper
limit of the model's performance. In this article, we present a novel framework
for automated machine learning. Our framework incorporates advances in dynamic
ensemble selection, and to our best knowledge, our approach is the first in the
field of AutoML to search and optimize ensemble strategies. In the comparison
experiments, our method outperforms the state-of-the-art automated machine
learning frameworks with the same CPU time in 42 classification datasets from
the OpenML platform. Ablation experiments on our framework validate the
effectiveness of our proposed method.
