A review of boosting methods for imbalanced data classification

Recently, the problem of imbalanced data classification has drawn a significant amount of interest from academia, industry and government funding agencies. The fundamental issue with imbalanced data classification is the imbalanced data has posed a significant drawback of the performance of most standard learning algorithms, which assume or expect balanced class distribution or equal misclassification costs. Boosting is a meta-technique that is applicable to most learning algorithms. This paper gives a review of boosting methods for imbalanced data classification, denoted as IDBoosting (Imbalanced-databoosting), where conventional learning algorithms can be integrated without further modifications. The main focus is on the intrinsic mechanisms without considering implementation detail. Existing methods are catalogued and each class is displayed in detail in terms of design criteria, typical algorithms and performance analysis. The essence of two IDBoosting methods is discovered followed by experimental evidence and useful reference point for future research are also given.