166 citations · 334 across the 10 of their papers we have counts for
Showing 2017 · cs.LGShow all
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cs.LG2017★ 63 cited
CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification
Farshid Rayhan, Sajid Ahmed, Asif Mahbub +3
Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing lea…
cs.LG2017
LIUBoost : Locality Informed Underboosting for Imbalanced Data Classification
Sajid Ahmed, Farshid Rayhan, Asif Mahbub +4
The problem of class imbalance along with class-overlapping has become a major issue in the domain of supervised learning. Most supervised learning algorithms assume equal cardinal…