DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique
By Chumphol Bunkhumpornpat, Krung Sinapiromsaran, Chidchanok Lursinsap. 2012
367 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Health Information Management
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Francisco Herrera
- Alberto Fernández
- Salvador Garcia
- Victoria López
- Vasile Palade
- Nitesh V. Chawla
- Seiichi Uchida
- Brian Kenji Iwana
In Health Information Management
- Alberto Fernández
- Nitesh V. Chawla
- Francisco Herrera
- Seiichi Uchida
- Brian Kenji Iwana
- Salvador Garcia
- Bartosz Krawczyk
Citations in the corpus
- Understanding Data Augmentation for Classification: When to Warp? (2016, methodology): “A more recent derivative of SMOTE is the Density Based SMOTE (DBSMOTE) algorithm [15], which generates the new synthetic samples within a distance eps of the cluster centre for the class (following original notation).”
- SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary (2018, methodology): “…based on geometric shapes, such as ellipses (Abdi & Hashemi, 2016) and voronoi diagrams (Young, Nykl, Weckman, & Chelberg, 2015), and graphs (Bunkhumpornpat et al., 2012); clustering-based interpolation (Barua, Islam, & Murase, 2011), in which the new examples can be either the centroids of…”
- An empirical survey of data augmentation for time series classification with neural networks (2020, methodology): “There have also been a number of improvements on SMOTE, such as Safe-Level-SMOTE [64], SMOTE based on the furthest neighbor (SMOTEFUNA) [65], Cost Minimization Oriented SMOTE (CMO-SMOTE) [66], Density-Based SMOTE (DBSMOTE) [67], etc. SMOTE has also been used in a feature space modeled by an an Echo ”
- An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (2013, methodology): “Some significant examples are the Cluster-Based Oversampling (CBO) [73], Class Purity Maximization [146], Sampling-Based Clustering [145], the agglomerative Hierarchical Clustering [34] or the DBSMOTE algorithm based on DBSCAN clustering [22].”