How to choose base classifier in ensembles? Recently, I have come through some papers, in which we could find a statement like this:
"We choose Hoeffding Classifier as the base classifier, and k=15 is set for the ensemble"..
According to my exposure, different algorithms could be used in an ensemble and a voting mechanism could be adapted. How can we use one same classifier in an ensemble for 15 times.? For example here:
https://www.cs.waikato.ac.nz/~abifet/MOA/API/_oza_bag_8java_source.html
Its using Hoeffding classifier by default. Could a classifier learn itself differently if called 2 times on same data??
Tx.

If you look at the API documentation for the code you linked to, it's an implementation of Oza and Russel (2001) Online Bagging and Boosting. This is an online algorithm that randomly selects one classifier, according to a $\text{Poisson}(1)$ distribution over the classifier index, to update for each training instance. Each of the base classifiers is therefore trained on a different partition of the data. The random choice is made in trainOnInstanceImpl of the code you linked.
