
Chapter 5
Evaluating Streaming Algorithms
Nowadays, several stream learning algorithms have been developed. Most of
them learn decision models that continuously evolve over time, run in resource-
aware environments, and detect and react to changes in the environment gen-
erating data. One important issue, not yet conveniently addressed, is the de-
sign of experimental work to evaluate and compare decision models that evolve
over time. In this chapter we present a general framework for assessing the
quality of streaming learning algorithms. We defend the use of Predictive Se-
quential error estimates over a sliding window to assess performance of learn- ...