Handling imbalanced datasets: A review

Abstract. Learning classifiers from imbalanced or skewed datasets is an important topic, arising very often in practice in classification problems. In such problems, almost all the instances are labelled as one class, while far fewer instances are labelled as the other class, usually the more important class. It is obvious that traditional classifiers seeking an accurate performance over a full range of instances are not suitable to deal with imbalanced learning tasks, since they tend to classify all the data into the majority class, which is usually the less important class. This paper describes various techniques for handling imbalance dataset problems. Of course, a single article cannot be a complete review of all the methods and algorithms, yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.