Learning, detecting, understanding, and predicting concept changes
Kyosuke Nishida, Koichiro Yamauchi · 2009
The demand for learning machines that can adapt to concept change, the change over time of the statistical properties of a target variable, has become more urgent. We, therefore, propose a system in which multiple online and offline classifiers are used for learning changing concepts. Our system is able to: respond to both sudden and gradual changes, handle recurring concepts, detect the occurrence of change, understand the hidden contexts of past concepts, and predict the next concept. We evaluate the effectiveness of our system's elements and demonstrate that our system performed well with synthetic concept-drifting and concept-shifting datasets.