Active Fuzzy Weighting Ensemble for Dealing with Concept Drift

DOI

10.2991/ijcis.11.1.33How to use a DOI?

Keywords

concept drift; change detection; ensemble learning; data streams

Abstract

The concept drift problem is a pervasive phenomenon in real-world data stream applications. It makes well-trained static learning models lose accuracy and become outdated as time goes by. The existence of different types of concept drift makes it more difficult for learning algorithms to track. This paper proposes a novel adaptive ensemble algorithm, the Active Fuzzy Weighting Ensemble, to handle data streams involving concept drift. During the processing of data instances in the data streams, our algorithm first identifies whether or not a drift occurs. Once a drift is confirmed, it uses data instances accumulated by the drift detection method to create a new base classifier. Then, it applies fuzzy instance weighting and a dynamic voting strategy to organize all the existing base classifiers to construct an ensemble learning model. Experimental evaluations on seven datasets show that our proposed algorithm can shorten the recovery time of accuracy drop when concept drift occurs, adapt to different types of concept drift, and obtain better performance with less computation costs than the other adaptive ensembles.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Fan Dong
AU  - Jie Lu
AU  - Guangquan Zhang
AU  - Kan Li
PY  - 2018
DA  - 2018/01/01
TI  - Active Fuzzy Weighting Ensemble for Dealing with Concept Drift
JO  - International Journal of Computational Intelligence Systems
SP  - 438
EP  - 450
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.33
DO  - 10.2991/ijcis.11.1.33
ID  - Dong2018
ER  -