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Data streams are often defined as large amounts of data flowing continuouslyat high speed. Moreover, these data are likely subject to changes in datadistribution, known as concept drift. Given all the reasons mentioned above,learning from streams is often online and under restrictions of memoryconsumption and run-time. Although many classification algorithms exist, mostof the works published in the area use Naive Bayes (NB) and Hoeffding Trees(HT) as base learners in their experiments. This article proposes an in-depthevaluation of k-Nearest Neighbors (k-NN) as a candidate for classifying datastreams subjected to concept drift. It also analyses the complexity in time andthe two main parameters of k-NN, i.e., the number of nearest neighbors used forpredictions (k), and window size (w). We compare different parameter values fork-NN and contrast it to NB and HT both with and without a drift detector (RDDM)in many datasets. We formulated and answered 10 research questions which led tothe conclusion that k-NN is a worthy candidate for data stream classification,especially when the run-time constraint is not too restrictive.

Evaluating k-NN in the Classification of Data Streams with Concept Drift