Overcoming feature drifts via dynamic feature weighted k-nearest neighbor learning
Jean Paul Barddal, Heitor Murilo Gomes, Jones Granatyr, Alceu de Souza Britto, Fabrício Enembreck · 2016
Extracting useful knowledge from data streams is problematic, mainly due to changes in their data distribution, a phenomenon named concept drift. Recently, studies have shown that most of existing algorithms for learning from data streams do not encompass techniques for a specific kind of drift: feature drifts. Feature drifts occur when features become, or cease to be, relevant to the learning task. In this paper, we propose an extension to the k-nearest neighbor classifier, so its distances' computations are weighted according to their current discriminative power. On our proposal, the discriminative power of features is given by entropy, which is swiftly computed over a sliding window. Empirical evidence shows that our approach is able to overcome several existing algorithms in accuracy and feature drift adaptation, while at the expense of bounded processing time and memory space.