A modified Learn++.NSE algorithm for dealing with concept drift

Concept drift is a very pervasive phenomenon in real world applications. By virtue of variety change types of concept drift, it makes more difficult for learning algorithm to track the concept drift very closely. Learn++.NSE is an incremental ensemble learner without any assumption on change type of concept drift. Even though it has good performance on handling concept drift, but it costs high computation and needs more time to recover from accuracy drop. This paper proposed a modified Learn++.NSE algorithm. During learning instances in data stream, our algorithm first identifies where and when drift happened, then uses instances accumulated by drift detection method to create a new base classifier, and finally organized all existing classifiers based on Learn++.NSE weighting mechanism to update ensemble learner. This modified algorithm can reduce high computation cost without any performance drop and improve the accuracy recover speed when drift happened.