REBAGG: REsampled BAGGing for Imbalanced Regression Tasks - LIDTA 2018

This repository has all the code used in the experiments carried out in the paper "REBAGG: REsampled BAGGing for Imbalanced Regression" [1].

This repository is organized as follows:

• Code folder - contains all the code for reproducing the experiments presented in the paper;
• Data folder - contains the 20 regression data sets used in the experiments carried out;

Requirements

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:

• DMwR
• performaceEstimation
• UBL
• uba
• randomForest
• e1071
• earth
• rpart
• gbm

All the above packages with the 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("DMwR", "performanceEstimation", "UBL", "randomForest", "e1071", "earth", "rpart", "gbm"))

The package uba needs to be installed from a tar.gz file that you can download from http://www.dcc.fc.up.pt/~rpribeiro/uba/. Download the tar.gz file into your folder and then issue:

install.packages("uba_0.7.8.tar.gz",repos=NULL,dependencies=T)

Check the other README files in each folder to see more detailed instructions on how to run the experiments.

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References

[1] Branco, P. and Torgo, L. and Ribeiro, R.P. (2018) "REBAGG: REsampled BAGGing for Imbalanced Regression" LIDTA2018: 2nd International Workshop on Learning with Imbalanced Domains: Theory and Applications (Co-located with ECML/PKDD 2018) Dublin, Ireland (to appear)