This repository has all the code used in the experiments carried out in the paper "Exploring Resampling with Neighborhood Bias on Imbalanced Regression Problems" [1].
This repository is organized as follows:
- R_Code folder - contains all the code for reproducing the experiments presented in the paper;
- Data folder - contains the 18 regression data sets used in the experiments carried out;
- Figures folder - contains all the extra figures obtained from the experimental evaluation carried out on 18 real world data sets;
- Tables folder - contains all the tables obtained from the experimental evaluation carried out on 18 real world data sets;
The experimental design was implemented in R language. Both code and data are in a format suitable for R environment.
In order to replicate these experiments you will need a working installation of R. Check [https://www.r-project.org/] if you need to download and install it.
In your R installation you also need to install the following additional R packages:
- e1071
- randomForest
- earth
- performanceEstimation
- UBL
- uba
All the above packages with exception of uba, can be installed from CRAN Repository directly as any "normal" R package. Essentially you need to issue the following command within R:
install.packages(c("e1071"", "randomForest", "earth", "performanceEstimation", "UBL"))
Additionally, you will need to install uba package from a tar.gz file that you can download from [http://www.dcc.fc.up.pt/~rpribeiro/uba/].
For installing this package issue the following command within R:
install.packages("uba_0.7.7.tar.gz",repos=NULL,dependencies=T)
To replicate the figures in this repository you will also need to install the package:
- ggplot2
As with any R package, we only need to issue the following command:
install.packages("ggplot2")
Check the other README files in each folder to see more detailed instructions on how to run the experiments.
[1] Paula Branco, Luís Torgo and Rita P. Ribeiro (2017). "Exploring Resampling with Neighborhood Bias on Imbalanced Regression Problems" 18th Portuguese Conference on Artificial Intelligence, EPIA 2017 (to appear).