-The class imbalanced problem occurs in various disciplines when one of target classes has a small number of instances compare to other classes. A classifier normally ignores or neglects to detect a minority class due to the small number of class instances. It poses a challenge to any classifier as it becomes hard to learn the minority class samples. Most of the oversampling methods may generat...

The acquisition of face images is usually limited due to policy and economy considerations, and hence the number of training examples of each subject varies greatly. The problem of face recognition with imbalanced training data has drawn attention of researchers and it is desirable to understand in what circumstances imbalanced data set affects the learning outcomes, and robust methods are need...

The Synthetic Minority Over Sampling TEchnique (SMOTE) is a widely used technique to balance imbalanced data. In this paper we focus on improving SMOTE in the presence of class noise. Many improvements of SMOTE have been proposed, mostly cleaning or improving the data after applying SMOTE. Our approach differs from these approaches by the fact that it cleans the data before applying SMOTE, such...

Roughly Balanced Bagging is based on under-sampling and classifies imbalanced data much better than other ensembles. In this paper, we experimentally study its properties that may influence its good performance. Results of experiments show that it can be constructed with a small number of component classifiers, which are quite accurate, however, of low diversity. Moreover, its good performance ...