A Survey of Predictive Modeling on Imbalanced Domains

Many real-world data-mining applications involve obtaining predictive models using datasets 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 modeling techniques. This article presents a survey of existing techniques for handling these important applications of predictive analytics. Although most…

• FP
  Fundação para a Ciência e a Tecnologia

  Award: PD/BD/105788/2014, SFRH/BSAB/113896/2015
• ER
  European Regional Development Fund

  Award: POCI-01-0145-FEDER-006961