Evaluating k-NN in the Classification of Data Streams with Concept Drift. (arXiv:2210.03119v1 [cs.LG])

Data streams are often defined as large amounts of data flowing continuously
at high speed. Moreover, these data are likely subject to changes in data
distribution, known as concept drift. Given all the reasons mentioned above,
learning from streams is often online and under restrictions of memory
consumption and run-time. Although many classification algorithms exist, most
of the works published in the area use Naive Bayes (NB) and Hoeffding Trees
(HT) as base learners in their experiments. This article proposes …

arxiv classification concept data data streams