63 citations · 86 across the 3 of their papers we have counts for
3 papers
cs.LG2018★ 23 cited
MEBoost: Mixing Estimators with Boosting for Imbalanced Data Classification
Farshid Rayhan, Sajid Ahmed, Asif Mahbub +4
Class imbalance problem has been a challenging research problem in the fields of machine learning and data mining as most real life datasets are imbalanced. Several existing machin…
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…