Borderline over-sampling for imbalanced data classification

By Hien M. Nguyen, Eric W. Cooper, Katsuari Kamei. Int. J. Knowl. Eng. Soft Data Paradigms, 2009

535 citations recorded by the source corpus.

Traditional classification algorithms usually provide poor accuracy on the prediction of the minority class of imbalanced data sets. This paper proposes a new method for dealing with imbalanced data sets by over-sampling the borderline minority class instances. A Support Vector Machine (SVM) classifier is then trained to predict future instances. Compared with other over-sampling methods, the proposed method focuses only on the minority class instances residing along the decision boundary, due to the fact that this region is the most crucial for establishing the decision boundary. Furthermore, the artificial minority instances are generated in such a way that the regions of the minority class with fewer majority class instances would be expanded by extrapolation, otherwise the current boundary of the minority class would be consolidated by interpolation. Experimental results show that the proposed method achieves a better performance than other over-sampling methods.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/5c0be11c0dfb22a50b59570a06768d0d86188057

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Citations in the corpus

Survey of resampling techniques for improving classification performance in unbalanced datasets (2016, methodology): “Some of these include One side selection [9], ADASYN [5], SVM SMOTE [12], SMOTEBoost [2], ClusterBased Oversampling [7], Kernel-based methods and active learning [6].”
A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning (2023, unknown): “Furthermore, a plethora of SMOTE extensions have been developed such as: Borderline SMOTE (Han et al., 2005), Safe-level SMOTE (Bunkhumpornpat et al., 2009), ADASYN (He et al., 2008), SVM SMOTE (Nguyen et al., 2011), Localized Random Affine Shadowsampling (LoRAS) (Bej et al., 2021), CDSMOTE (Elyan e”
A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data (2021, methodology): “SMOTE-SVM [48], [68] instead focuses on creating samples on the decision borders of minority and majority classes created by the SVM classiﬁer.”
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning (2016, methodology): “Diﬀerent variants of this algorithm have been proposed: SMOTE borderline 1 & 2 (Han et al., 2005) and SMOTE SVM (Nguyen et al., 2011).”