DBSMOTE: Density-based synthetic minority over-sampling technique

• Main Authors: Bunkhumpornpat,C., Sinapiromsaran,K., Lursinsap,C.
• Format: Article
• Published: Springer Netherlands 2015
• Subjects: Artificial Intelligence
• Online Access: http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84862140885&origin=inward; http://cmuir.cmu.ac.th/handle/6653943832/38631
• Institution: Chiang Mai University

A dataset exhibits the class imbalance problem when a target class has a very small number of instances relative to other classes. A trivial classifier typically fails to detect a minority class due to its extremely low incidence rate. In this paper, a new over-sampling technique called DBSMOTE is proposed. Our technique relies on a density-based notion of clusters and is designed to over-sample an arbitrarily shaped cluster discovered by DB-SCAN. DBSMOTE generates synthetic instances along a shortest path from each positive instance to a pseudo-centroid of a minority-class cluster. Consequently, these synthetic instances are dense near this centroid and are sparse far from this centroid. Our experimental results show that DBSMOTE improves precision, F-value, and AUC more effectively than SMOTE, Borderline-SMOTE, and Safe-Level-SMOTE for imbalanced datasets. © 2011 Springer-Verlag.