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ebmc  

Ensemble-Based Methods for Class Imbalance Problem
View on CRAN: Click here


Download and install ebmc package within the R console
Install from CRAN:
install.packages("ebmc")

Install from Github:
library("remotes")
install_github("cran/ebmc")

Install by package version:
library("remotes")
install_version("ebmc", "1.0.1")



Attach the package and use:
library("ebmc")
Maintained by
"Hsiang Hao, Chen"
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-08-29
Latest Update: 2022-01-10
Description:
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) , Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, and Amri Napolitano (2010) , R. Barandela, J. S. Sanchez, R. M. Valdovinos (2003) , Shuo Wang and Xin Yao (2009) , Yoav Freund and Robert E. Schapire (1997) .
How to cite:
"Hsiang Hao, Chen" (2017). ebmc: Ensemble-Based Methods for Class Imbalance Problem. R package version 1.0.1, https://cran.r-project.org/web/packages/ebmc. Accessed 05 Jan. 2025.
Previous versions and publish date:
1.0.0 (2017-08-29 17:44)
Other packages that cited ebmc R package
View ebmc citation profile
Other R packages that ebmc depends, imports, suggests or enhances
Complete documentation for ebmc
Functions, R codes and Examples using the ebmc R package
Some associated functions: adam2 . measure . predict.modelBag . predict.modelBst . rus . sbag . sbo . ub . 
Some associated R codes: ebmc.R .  Full ebmc package functions and examples
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