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Found 3 papers, 1 papers with code
MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification
The performance of MEBoost has been evaluated on 12 benchmark imbalanced datasets with state of the art ensemble methods like SMOTEBoost, RUSBoost, Easy Ensemble, EUSBoost, DataBoost.
CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification
We evaluated the performance of CUSBoost algorithm with the state-of-the-art methods based on ensemble learning like AdaBoost, RUSBoost, SMOTEBoost on 13 imbalance binary and multi-class datasets with various imbalance ratios.
LIUBoost : Locality Informed Underboosting for Imbalanced Data Classification
The problem of class imbalance along with class-overlapping has become a major issue in the domain of supervised learning.
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