Standardized Evaluation of Machine Learning Methods for Evolving Data Streams. (arXiv:2204.13625v1 [cs.LG])

Due to the unspecified and dynamic nature of data streams, online machine
learning requires powerful and flexible solutions. However, evaluating online
machine learning methods under realistic conditions is difficult. Existing work
therefore often draws on different heuristics and simulations that do not
necessarily produce meaningful and reliable results. Indeed, in the absence of
common evaluation standards, it often remains unclear how online learning
methods will perform in practice or in comparison to similar work. In this
paper, we propose a …

arxiv data data streams evaluation learning machine machine learning