Code for the paper: Extended Federated Ensemble Regression using Classification (EFERUC).

To reproduce the EFERUC results having copied the correct codebase in the EFERUC folder to a local directory:

1. Download the experiments datasets.zip folder from http://dx.doi.org/10.17632/mpvwnhv4vb.2
2. There are four folders in the experiments datasets folder downloaded in (1). To reproduce the gene expression experiments, copy the gene_expression dataset folder into the gene expression folder in this repository and rename it to datasets. It should be in the same folder level as the code folder. To run the experiments, go into the code folder and execute the eferuc.R script. It will automatically create an output folder in the same level as the code and datasets folders. The same process should be followed for the other dataset folders, however, the general folder in this repository should be used instead of the gene_expression folder. Some code folders have _nominal attached to them. This indicates that the datasets with nominal attributes should be performed using the code in that folder.

The output folder has the following structure:

+-- output
|   +-- dataset_name_A
|   |   +-- weight_details
|   |   +-- best_bin_size
|   |   +-- performance
|   |   |   +-- regression
|   |   |   +-- classification
|   |   |   |   +-- RDS
|   +-- dataset_name_B
|   ...
|   +-- dataset_name_C

To reproduce the resampling results use resampler_script.R in the Resampling folder. Perform the following:

1. Download datasets_and_splits.zip from https://data.mendeley.com/datasets/mpvwnhv4vb/2 and unpack in your working directory. These are the datasets and training/test splits used in the paper.
2. Download the Code and Data folder from [https://www.dcc.fc.up.pt/~ltorgo/ExpertSystems/] and unpack in your working directory. This is code accompanying the paper by Torgo et al.
3. You will need to install the uba package in order to use the relevance function. Note that one of the dependencies of the package has been taken of CRAN so you will need to install it by hand by first downloading the freshest version here and then running install.packages( "Path/To/DMwR_0.4.1.tar.gz", repos=NULL, type="source" ).
4. Make sure smoter_helper.R, nominal_var_info.rds and relevance.rds are in your working directory, or amend the script (lines 9, 20, 22) so that it knows where it is.
5. To run an experiment (for all the learners: ranger, xgboost, lasso and ridge) for data collection d, dataset a, resampling method Smoter (s=TRUE) or undersampler (s=FALSE), undersampling level u and, for SmoteR, oversampling level o, execute: Rscript --vanilla resampler_script.R d=d a=a s=s u=u o=o

• dataset collections are: Yeast, QSAR, OpenML, gene_expression and PaoBrancoImbalanced, corresponding to the four sub-folders in the datasets_and_splits/datasets folder.
• dataset names is the first portion of the file names in datasets_and_splits/datasets/ subfolders, e.g. for yeast_x5.Fluorocytosine_X.csv, it is yeast_x5.Fluorocytosine.
• to run SmoteR, choose s=TRUE, else, to run undersampler, choose s=FALSE.
• undersampling and oversampling levels can be freely chosen by the user.

Example use: Rscript --vanilla resampler_script.R d=Yeast a=yeast_x4NQO s=TRUE u=50 o=100

5. Results will be saved in output in the format d_a_l_s_u_o_mod.rds (model fitted on a resampled dataset), d_a_l_s_u_o_pred.rds (prediction obtained on the test set) and d_a_l_s_u_o_rsq.rds (R-squared).

To reproduce analysis performed in the paper, user will need to run resampler_script.R for all dataset collections, resampling methods and combinations of under- and oversampling detailed in our paper.