Learning imbalanced classes in the presence of concept growth
Wing Yee Sit, Kezhi Mao · 2013
Many practical scenarios see a concept growth problem rather than the well-known concept drift problem. Applications with imbalanced classes are also common, but the problem is seldom considered. This paper proposes a cognitively inspired classification system to handle the difficulties that arise, and shows marked improvements in the classification results.