Recently I'm working on some online learning algorithm (using RBF neural network ) for classification. As I read papers in this area I found there is an issue in online-learning called concept drift problem which my algorithm has and I have to find a way and solve this problem. So I am trying to find out what is the best way to tackle the issue of concept drift and I found approaches like sliding window, drift detection, ensemble learning and etc. Between these methods, ensemble learning is interesting for me so I watched this tutorial, I found out that this method is sampling from dataset and then gives it to different learning algorithms.
This method presented to solve concept drifting problem in online-learning( data coming one by one and train model then discarded) and it samples from the dataset, giving to different learning algorithm so we need to have a dataset already. Having dataset before and online-learning have conflict with each other. So is ensemble algorithm use in bach or online learning ?