README.md

In nunompmoniz/ReBoost: Boosting by Resampling Approaches for Imbalanced Numerical Prediction Tasks

ReBoost

An R Package with Boosting and SMOTEBoost implementations for Regression Tasks

Data pre-processing methods such as resampling strategies are the most common approach to tackling imbalanced domain learning tasks. This package encompasses multiple resampling-based boosting strategies for the task of extreme value prediction/imbalanced regression.

References

• Nuno Moniz, Rita Ribeiro, Vitor Cerqueira, Nitesh Chawla (2018). "SMOTEBoost for Regression: Improving the Prediction of Extreme Values", Proceedings of the 2018 IEEE 5th International Conference on Data Science and Advanced Analytics.

To install from github use the following command lines in R:

library(devtools)  # You need to install this package!
install_github("nunompmoniz/ReBoost",ref="master")

After installation the package can be used loaded by doing:

 library(ReBoost)

nunompmoniz/ReBoost documentation built on Sept. 15, 2021, 8:28 p.m.