  In this paper, the issue of tailoring the soft confusion matrix (SCM) based
classifier to deal with stream learning task is addressed. The main goal of the
work is to develop a wrapping-classifier that allows incremental learning to
classifiers that are unable to learn incrementally. The goal is achieved by
making two improvements in the previously developed SCM classifier. The first
one is aimed at reducing the computational cost of the SCM classifier. To do
so, the definition of the fuzzy neighborhood of an object is changed. The
second one is aimed at effective dealing with the concept drift. This is done
by employing the ADWIN-driven concept drift detector that is not only used to
detect the drift but also to control the size of the neighbourhood. The
obtained experimental results show that the proposed approach significantly
outperforms the reference methods.
