  Many real world data mining applications involve obtaining predictive models
using data sets with strongly imbalanced distributions of the target variable.
Frequently, the least common values of this target variable are associated with
events that are highly relevant for end users (e.g. fraud detection, unusual
returns on stock markets, anticipation of catastrophes, etc.). Moreover, the
events may have different costs and benefits, which when associated with the
rarity of some of them on the available training data creates serious problems
to predictive modelling techniques. This paper presents a survey of existing
techniques for handling these important applications of predictive analytics.
Although most of the existing work addresses classification tasks (nominal
target variables), we also describe methods designed to handle similar problems
within regression tasks (numeric target variables). In this survey we discuss
the main challenges raised by imbalanced distributions, describe the main
approaches to these problems, propose a taxonomy of these methods and refer to
some related problems within predictive modelling.
