A Research Review of the Stacking Classification Model

The Stacking Classification Model (SCM) is an ensemble learning method that improves classification performance by integrating multiple base learners. SCM has gained popularity as a machine learning research topic because of its remarkable performance in challenging tasks. In this article we give a research review of the SCM. At the beginning, we address the development of the stacking model and examine its key benefits and drawbacks by contrasting it with traditional homogeneous ensemble learning models like bagging and boosting. Then we focus on the Algorithm Structure and mathematical principles of the SCM. Finally, we look at possible research trends and future development paths for the SCM.