Combining Base-Learners into Ensembles

Abstract

This chapter discusses ensembles of classification or regression models, because they represent an important area of machine learning. They have become popular as they tend to achieve high performance when compared with single models. Besides, they also play an essential role in data-streaming solutions. This chapter starts by introducing ensemble learning and presents an overview of some of its most well-known methods. These include bagging, boosting, stacking, cascade generalization, cascading, delegating, arbitrating and meta-decision trees.