Change detection in categorical evolving data streams
Dino Ienco, Albert Bifet, Bernhard Pfahringer, Pascal Poncelet · 2014
Detecting change in evolving data streams is a central issue for accurate adaptive learning. In real world applications, data streams have categorical features, and changes induced in the data distribution of these categorical features have not been considered extensively so far. Previous work on change detection focused on detecting changes in the accuracy of the learners, but without considering changes in the data distribution.