TY - GEN
T1 - A modified Learn++.NSE algorithm for dealing with concept drift
AU - Dong, Fan
AU - Lu, Jie
AU - Zhang, Guangquan
AU - Li, Kan
N1 - Publisher Copyright:
© 2014 by World Scientific Publishing Co. Pte. Ltd. All rights reserved.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85037364780&partnerID=8YFLogxK
U2 - 10.1142/9789814619998_0092
DO - 10.1142/9789814619998_0092
M3 - Conference contribution
AN - SCOPUS:85037364780
T3 - Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014
SP - 556
EP - 561
BT - Decision Making and Soft Computing - Proceedings of the 11th International FLINS Conference, FLINS 2014
A2 - de Moraes, Ronei Marcos
A2 - Kerre, Etienne E.
A2 - dos Santos Machado, Liliane
A2 - Lu, Jie
PB - World Scientific Publishing Co. Pte Ltd
T2 - Decision Making and Soft Computing - 11th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2014
Y2 - 17 August 2014 through 20 August 2014
ER -