Evaluating k-NN in the Classification of Data Streams with Concept Drift
This work addresses the problem of selecting effective classifiers for data streams with concept drift, but it is incremental as it focuses on evaluating an existing method (k-NN) rather than introducing new techniques.
The paper evaluated k-Nearest Neighbors (k-NN) for classifying data streams with concept drift, comparing it to Naive Bayes and Hoeffding Trees, and concluded that k-NN is a viable option when run-time constraints are not too strict.
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 an in-depth evaluation of k-Nearest Neighbors (k-NN) as a candidate for classifying data streams subjected to concept drift. It also analyses the complexity in time and the two main parameters of k-NN, i.e., the number of nearest neighbors used for predictions (k), and window size (w). We compare different parameter values for k-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 to the conclusion that k-NN is a worthy candidate for data stream classification, especially when the run-time constraint is not too restrictive.