  In big data era, the data continuously generated and its distribution may
keep changes overtime. These challenges in online stream of data are known as
concept drift. In this paper, we proposed the Adaptive Convolutional ELM method
(ACNNELM) as enhancement of Convolutional Neural Network (CNN) with a hybrid
Extreme Learning Machine (ELM) model plus adaptive capability. This method is
aimed for concept drift handling. We enhanced the CNN as convolutional
hiererchical features representation learner combined with Elastic ELM
(E$^2$LM) as a parallel supervised classifier. We propose an Adaptive OS-ELM
(AOS-ELM) for concept drift adaptability in classifier level (named ACNNELM-1)
and matrices concatenation ensembles for concept drift adaptability in ensemble
level (named ACNNELM-2). Our proposed Adaptive CNNELM is flexible that works
well in classifier level and ensemble level while most current methods only
proposed to work on either one of the levels.
  We verified our method in extended MNIST data set and not MNIST data set. We
set the experiment to simulate virtual drift, real drift, and hybrid drift
event and we demonstrated how our CNNELM adaptability works. Our proposed
method works well and gives better accuracy, computation scalability, and
concept drifts adaptability compared to the regular ELM and CNN. Further
researches are still required to study the optimum parameters and to use more
varied image data set.
