  Mining data streams poses a number of challenges, including the continuous
and non-stationary nature of data, the massive volume of information to be
processed and constraints put on the computational resources. While there is a
number of supervised solutions proposed for this problem in the literature,
most of them assume that access to the ground truth (in form of class labels)
is unlimited and such information can be instantly utilized when updating the
learning system. This is far from being realistic, as one must consider the
underlying cost of acquiring labels. Therefore, solutions that can reduce the
requirements for ground truth in streaming scenarios are required. In this
paper, we propose a novel framework for mining drifting data streams on a
budget, by combining information coming from active learning and self-labeling.
We introduce several strategies that can take advantage of both intelligent
instance selection and semi-supervised procedures, while taking into account
the potential presence of concept drift. Such a hybrid approach allows for
efficient exploration and exploitation of streaming data structures within
realistic labeling budgets. Since our framework works as a wrapper, it may be
applied with different learning algorithms. Experimental study, carried out on
a diverse set of real-world data streams with various types of concept drift,
proves the usefulness of the proposed strategies when dealing with highly
limited access to class labels. The presented hybrid approach is especially
feasible when one cannot increase a budget for labeling or replace an
inefficient classifier. We deliver a set of recommendations regarding areas of
applicability for our strategies.
