Homogeneous vs heterogeneous ensembles

Question

I would like to check with you if my understanding about ensemble learning (homogeneous vs heterogeneous) is correct.

Is the following statement correct?

An homogeneous ensemble is a set of classifiers of the same type built upon different data as random forest and an heterogeneous ensemble is a set of classifiers of different types built upon same data.

If it's not correct, could you please clarify this point?

Answer 1

Homogeneous ensemble consists of members having a single-type base learning algorithm. Popular methods like bagging and boosting generate diversity by sampling from or assigning weights to training examples but generally utilize a single type of base classifier to build the ensemble.

On the other hand, Heterogeneous ensemble consists of members having different base learning algorithms such as SVM, ANN and Decision Trees. A popular heterogeneous ensemble method is stacking, which is similar to boosting.

This table contains examples for both homogeneous and heterogeneous ensemble models.

EDIT:

Homogeneous ensemble methods, use the same feature selection method with different training data and distributing the dataset over several nodes while Heterogeneous ensemble methods use different feature selection methods with the same training data.