IntroductionImbalanced data classif

Introduction
Imbalanced data classification often arises in many
practical applications in the context of medical pattern
recognition and data mining. Most of the existing
state-of-the-art classification approaches are well
developed by assuming the underlying training set is
evenly distributed. However, they are faced with a
severe bias problem when the training set is a highly
imbalanced distribution (i.e., the data comprises two
classes, the minority class C+ and the majority class
C