Combining similarity in time and space for training set formation under concept drift
Indrė Žliobaitė · Intelligent Data Analysis · 2011
Concept drift is a challenge in supervised learning for sequential data. It describes a phenomenon when the data distributions change over time. In such a case accuracy of a classifier benefits from the selective sampling for training. We develop a m