Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning

By Hui Han, Wenyuan Wang, Binghuan Mao. 2005

4,118 citations recorded by the source corpus.

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Citations in the corpus

Long-Tailed Class Incremental Learning (2022, methodology): “Learning from long-tailed distributions has been approach by re-sampling [11,15] or re-weighting [21,36,48] head classes and tail classes to learn a balanced classifier.”
Co2L: Contrastive Continual Learning (2021, background): “As the initial or new task arrives, the dataset is built as a union of current task samples and buffered samples, without any oversampling [9, 19].”
Decoupling Representation and Classifier for Long-Tailed Recognition (2019, methodology): “These methods include over-sampling (Chawla et al., 2002; Han et al., 2005) for the minority classes (by adding copies of data), under-sampling (Drummond et al., 2003) for the majority classes (by removing data), and class-balanced sampling (Shen et al., 2016; Mahajan et al., 2018) based on the…”
A systematic study of the class imbalance problem in convolutional neural networks (2017, methodology): “Some extensions of this technique were proposed, for example focusing only on examples near the boundary between classes [35].”
A novel ensemble method for classifying imbalanced data (2015, background): “[67] proposed the borderline-SMOTE to oversample the minority class instances near the borderline.”
Machine Learning with Oversampling and Undersampling Techniques: Overview Study and Experimental Results (2020, methodology): “An example of oversampling methods is Borderline-SMOTE [9].”
SMOTE-RSB*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory (2012, methodology): “This method only oversamples or strengthens the borderline minority examples [22].”
Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection (2021, background): “SMOTE [5] is a representative interpolation-based over-sampling methods and also has lots of extensions [12, 13].”
Conversation Graph: Data Augmentation, Training, and Evaluation for Non-Deterministic Dialogue Management (2020, background): “Downsampling is related to SMOTe, the Synthetic Minority Oversampling Technique (Chawla et al., 2002), particularly its later variants (Han et al., 2005), which aim to balance the training data by oversampling rare cases.”
Survey of resampling techniques for improving classification performance in unbalanced datasets (2016, methodology): “achieve r= 0:5. jLj jSj Before resampling 6320 680 After resampling 6320 3160 precision on L recall on S 0.97 0.77 4.5.3 Borderline-SMOTE1 There are two enhancements of SMOTE, termed borderline SMOTE [3], which may yield better performance than vanilla SMOTE. For each point pin S: 1.Compute its mnea”
Experimental perspectives on learning from imbalanced data (2007, methodology): “’s SMOTE technique which they call borderline-SMOTE (Han et al., 2005) (BSM).”
A Survey of Predictive Modeling on Imbalanced Domains (2016, methodology): “Examples of these approaches are as follows: Borderline-SMOTE [Han et al. 2005], Adaptive Synthetic (ADASYN) [He et al. 2008], Modiﬁed Synthetic Minority Oversampling Technique (MSMOTE) [Hu et al. 2009], Majority Weighted Minority Oversampling TEchnique (MWMOTE) [Barua et al. 2012], For regression…”
A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data (2021, background): “Borderline-SMOTE algorithm [47], [67] creates new points as linear combinations of the borderline minor class points.”
Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases (2016, methodology): “Borderline-SMOTE: BorderlineSynthetic Minority Oversampling TEchnique [53] oversamples the borderline of the minority class by applying SMOTE.”