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This study brings together systematised views of two related areas: data editing for the nearest neighbour classifier and adaptive learning in the presence of concept drift. The growing number of studies in the intersection of these areas warrants a closer look. We revise and update the taxonomies of the two areas proposed in the literature and argue that they are not sufficiently discriminative with...
We aim to dispel the blind faith in theoretical criteria for optimisation of the edited nearest neighbour classifier and its version called the Voronoi classifier. Three criteria from past and recent literature are considered: two bounds using Vapnik-Chervonenkis (VC) dimension and a probabilistic criterion derived by a Bayesian approach. We demonstrate the shortcomings of these criteria for selecting...
While there is a lot of research on change detection based on the streaming classification error, finding changes in multidimensional unlabelled streaming data is still a challenge. Here we propose to apply principal component analysis (PCA) to the training data, and mine the stream of selected principal components for change in the distribution. A recently proposed semi-parametric log-likelihood...
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