Majority-Class Aware Support Vector Domain Oversampling for Imbalanced Classification Problems

摘要

In this work, a method is presented to overcome the difficulties posed by imbalanced classification problems. The proposed algorithm fits a data description to the minority class but in contrast to many other algorithms, awareness of samples of the majority class is used to improve the estimation process. The majority samples are incorporated in the optimization procedure and the resulting domain descriptions are generally superior to those without knowledge about the majority class. Extensive experimental results support the validity of this approach.