Clustering-based undersampling in class-imbalanced data

By Wei‐Chao Lin, Chih-Fong Tsai, Ya-Han Hu, Jing-Shang Jhang. Information Sciences, 2017

783 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/4ef7de98de21d49ad76bf6c47897b66c536aca11

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Citations in the corpus

Oversampling for Imbalanced Learning Based on K-Means and SMOTE (2017, methodology): “The undersampling step of this method is identical to cluster-based undersampling [32] retaining cluster medoids with the number of clusters k set to the mean class size.”
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, methodology): “Lin et al. [263] proposed two strategies to undersample the data using clustering.”
A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining (2023, methodology): “…techniques such as ENN [24] were also found to work well for datasets with a low to moderate imbalance ratio, while the other SMOTE variants such as polynomial SMOTE [25] could handle both datasets with a moderately imbalanced class and an extremely imbalanced class equally well [11,26].”
Ensembles for feature selection: A review and future trends (2019, neutral): “…to the new problems that arise, mostly, derived from the Big data phenomenon, such as streaming processing , supporting incremental learning [99, 100, 101, 102, 103], the problem of imbalanced data [104, 105], missing data [10, 106] or the need for distributed and parallel learning [107, 108, 109].”
A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset (2018, methodology): “…achieved 86.8% in AUC for the KBD dataset and outperforms several methods for dealing with the imbalance data problem for bankruptcy prediction such as GMBoost algorithm [24], the oversampling-based method using SMOTEENN [28], and the clustering-based undersampling method [20] in terms of accuracy.”
DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data (2021, background): “While not many solutions have been proposed for guided undersampling [14], [15], [16], oversampling has gained much more attention due to the success of SMOTE [8], which led to the introduction of a plethora of variants [17], [18], [19].”