Affiliations: Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
Correspondence:
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Corresponding author: Hamid Beigy, Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. E-mail:beigy@sharif.edu
Abstract: Concept drift, change in the underlying
distribution that data points come from, is an inevitable phenomenon in data
streams. Due to increase in the number of data streams' applications such as
network intrusion detection, weather forecasting, and detection of
unconventional behavior in financial transactions; numerous researches have
recently been conducted in the area of concept drift detection. An ideal
method for concept drift detection should be able to rapidly and correctly
identify changes in the underlying distribution of data points and adapt its
model as quickly as possible while the memory and processing time is
limited. In this paper, we propose a novel explicit method based on ensemble
classifiers for detecting concept drift. The method processes samples one by
one, and monitors the distribution of ensemble's error in order to detect
probable drifts. After detection of a drift, a new classifier will be
trained on the new concept in order to keep the model up-to-date. The
proposed method has been evaluated on some artificial and real benchmark data sets. The experiments' results show that the proposed method is capable
of detecting and adjusting to concept drifts from different types, and it
has outperformed well-known state-of-the-art methods. Especially, in the
case of high-speed concept drifts.
