TY - JOUR
AU - Lursinsap, Chidchanok
AB - 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 DBSCAN. 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.
TI - DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique
JF - Applied Intelligence
DO - 10.1007/s10489-011-0287-y
DA - 2011-04-14
UR - https://www.deepdyve.com/lp/springer-journals/dbsmote-density-based-synthetic-minority-over-sampling-technique-0jakuvfDX7
SP - 664
EP - 684
VL - 36
IS - 3
DP - DeepDyve
ER -