A Preliminary Study on the Selection of Generalized Instances for Imbalanced Classification

Learning in imbalanced domains is one of the recent challenges in machine learning and data mining. In imbalanced classification, data sets present many examples from one class and few from the other class, and the latter class is the one which receives more interest from the point of view of learning. One of the most used techniques to deal with this problem consists in preprocessing the data previously to the learning process.