Four ensemble-based methods (SMOTEBoost, RUSBoost, UnderBagging, and SMOTEBagging) for class imbalance problem are implemented for binary classification. Such methods adopt ensemble methods and data re-sampling techniques to improve model performance in presence of class imbalance problem. One special feature offers the possibility to choose multiple supervised learning algorithms to build weak learners within ensemble models. References: Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, and Kevin W. Bowyer (2003)
|Author||Hsiang Hao, Chen|
|Date of publication||2017-08-29 15:44:12 UTC|
|Maintainer||"Hsiang Hao, Chen" <[email protected]>|
|License||GPL (>= 3)|
|Package repository||View on CRAN|
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