Unsupervised Concept Drift Detection based on Parallel Activations of Neural Network

arXiv:2404.07776v1 Announce Type: new
Abstract: Practical applications of artificial intelligence increasingly often have to deal with the streaming properties of real data, which, considering the time factor, are subject to phenomena such as periodicity and more or less chaotic degeneration - resulting directly in the concept drifts. The modern concept drift detectors almost always assume immediate access to labels, which due to their cost, limited availability and possible delay has been shown to be unrealistic. This work proposes an unsupervised …

abstract applications applications of artificial intelligence artificial artificial intelligence arxiv concept cs.lg data deal detection drift intelligence modern network neural network practical real data streaming type unsupervised