  Concept Drift (CD) detection intends to continuously identify changes in data
stream behaviors, supporting researchers in the study and modeling of
real-world phenomena. Motivated by the lack of learning guarantees in current
CD algorithms, we decided to take advantage of the Statistical Learning Theory
(SLT) to formalize the necessary requirements to ensure probabilistic learning
bounds, so drifts would refer to actual changes in data rather than by chance.
As discussed along this paper, a set of mathematical assumptions must be held
in order to rely on SLT bounds, which are especially controversial in CD
scenarios. Based on this issue, we propose a methodology to address those
assumptions in CD scenarios and therefore ensure learning guarantees.
Complementary, we assessed a set of relevant and known CD algorithms from the
literature in light of our methodology. As main contribution, we expect this
work to support researchers while designing and evaluating CD algorithms on
different domains.
