… However, they do not imply that classifiers cannot learn from imbalanced data sets; on the
… certain imbalanced data sets are comparable to classifiers induced from the same data set …

… research challenges standing before learning from imbalanced data that have roots …
imbalanced learning such as classification, clustering, regression, mining data streams and big data …

Learning with imbalanced data refers to the scenario in which the amounts of instances that
represent the concepts in a given problem follow a different distribution. The main issue …

… that emerged when machine learning matured from an … in the machine learning/data
mining research circles about a … that their data sets were imbalanced and that this imbalance …

We present a comprehensive suite of experimentation on the subject of learning from
imbalanced data. When classes are imbalanced, many learning algorithms can suffer from the …

… of the classification of imbalanced data that includes … imbalanced learning and propose a
general framework within which each algorithm can be placed. This framework is a unified data …

For research to progress most effectively, we first should establish common ground regarding
just what is the problem that imbalanced data sets present to machine learning systems. …

… -rate gap between balanced and imbalanced data sets. This suggests that the imbalance
problem is a relative problem (ie, it depends on the proportion of imbalance experienced by the …

… method as AL since it is the only active learning method that we used afterwards. In order to
… to imbalanced data classification, we examine its performance by varying class imbalance …

… sifiers in the context of imbalanced data has been reported in … evaluation of RF learners
built from imbalanced data. What … How does the RF learner perform on imbalanced data when …