  One of the significant problems of streaming data classification is the
occurrence of concept drift, consisting of the change of probabilistic
characteristics of the classification task. This phenomenon destabilizes the
performance of the classification model and seriously degrades its quality. An
appropriate strategy counteracting this phenomenon is required to adapt the
classifier to the changing probabilistic characteristics. One of the
significant problems in implementing such a solution is the access to data
labels. It is usually costly, so to minimize the expenses related to this
process, learning strategies based on semi-supervised learning are proposed,
e.g., employing active learning methods indicating which of the incoming
objects are valuable to be labeled for improving the classifier's performance.
This paper proposes a novel chunk-based method for non-stationary data streams
based on classifier ensemble learning and an active learning strategy
considering a limited budget that can be successfully applied to any data
stream classification algorithm. The proposed method has been evaluated through
computer experiments using both real and generated data streams. The results
confirm the high quality of the proposed algorithm over state-of-the-art
methods.
