Inner ensembles: Using ensemble methods inside the learning algorithm

Abstract

Ensemble Methods represent an important research area within machine learning. Here, we argue that the use of such methods can be generalized and applied in many more situations than they have been previously. Instead of using them only to combine the output of an algorithm, we can apply them to the decisions made inside the learning algorithm, itself. We call this approach Inner Ensembles. The main contribution of this work is to demonstrate how broadly this idea can applied. Specifically, we show that the idea can be applied to different classes of learner such as Bayesian networks and K-means clustering. © 2013 Springer-Verlag.