DBSMOTE: Density-based synthetic minority over-sampling technique
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 p...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Springer Netherlands
2015
|
| Subjects: | |
| Online Access: | http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84862140885&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/38631 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Chiang Mai University |
| Summary: | 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. |
|---|