  When constructing a classifier ensemble, diversity among the base classifiers
is one of the important characteristics. Several studies have been made in the
context of standard static data, in particular, when analyzing the relationship
between a high ensemble predictive performance and the diversity of its
components. Besides, ensembles of learning machines have been performed to
learn in the presence of concept drift and adapt to it. However, diversity
measures have not received much research interest in evolving data streams.
Only a few researchers directly consider promoting diversity while constructing
an ensemble or rebuilding them in the moment of detecting drifts. In this
paper, we present a theoretical analysis of different diversity measures and
relate them to the success of ensemble learning algorithms for streaming data.
The analysis provides a deeper understanding of the concept of diversity and
its impact on online ensemble Learning in the presence of concept drift. More
precisely, we are interested in answering the following research question;
Which commonly used diversity measures are used in the context of static-data
ensembles and how far are they applicable in the context of streaming data
ensembles?
