  Based on the experimental results, all concepts drift types have their
respective hyperparameter configurations. Simple and gradual concept drift have
similar pattern which requires a smaller {\alpha} value than recurring concept
drift because, in this type of drift, a new concept appear continuously, so it
needs a high-frequency model adaptation. However, in recurring concepts, the
new concept may repeat in the future, so the lower frequency adaptation is
better. Furthermore, high-frequency model adaptation could lead to an
overfitting problem. Implementing CMGMM component pruning mechanism help to
control the number of the active component and improve model performance.
