Sampling methods for imbalanced regression. Implemented resampling methods are:

1- SMOTER (Luís Torgo et al. 2013)

2- SMOGN (Branco, Torgo, and Ribeiro 2017)

3- Random over-under sampling

4- WERSC (Branco, Torgo, and Ribeiro 2019)

5- GNO (Gaussian Noise Oversampling) (Branco, Torgo, and Ribeiro 2019)

6- GSMOTER (Geometric SMOTE for Regression) (Camacho, Douzas, and Bacao 2022)

7- SMOTERWB (SMOTER with Boosting)

Implemented metrics are:

1- WSSE (weighted sum of squared error)

2- WMSE (weighted mean squared error)

3- WMAD (weighted mean absolute deviation)

4- WMAPE (weighted mean absolute percentage error)

5- MU (Mean utility scores) (Luis Torgo and Ribeiro 2007)

6- NMU (Normalized mean utility scores) (Luis Torgo and Ribeiro 2007)

7- Precision for regression (Luis Torgo and Ribeiro 2007)

8- Recall for regression (Luis Torgo and Ribeiro 2007)

9- F_β for regression (Luis Torgo and Ribeiro 2007)

Implemented relevance functions are:

1- PCHIP (Piecewise Cubic Hermite Interpolating Polynomials) (Luis Torgo and Ribeiro 2007)

2- Inverse Kernel Density Estimation (Steininger et al. 2021)

3- Sigmoid relevance (Luis Torgo and Ribeiro 2009)

More methods will be added.

devtools::install_github(“https://github.com/fatihsaglam/ImbRegSamp”)

Branco, Paula, Luis Torgo, and Rita P. Ribeiro. 2019. “Pre-Processing Approaches for Imbalanced Distributions in Regression.” Neurocomputing 343: 76–99. https://doi.org/https://doi.org/10.1016/j.neucom.2018.11.100.

Branco, Paula, Luís Torgo, and Rita P. Ribeiro. 2017. “SMOGN: A Pre-Processing Approach for Imbalanced Regression.” In Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, edited by Paula Branco Luís Torgo and Nuno Moniz, 74:36–50. Proceedings of Machine Learning Research. PMLR. https://proceedings.mlr.press/v74/branco17a.html.

Camacho, Luís, Georgios Douzas, and Fernando Bacao. 2022. “Geometric SMOTE for Regression.” Expert Systems with Applications 193: 116387. https://doi.org/https://doi.org/10.1016/j.eswa.2021.116387.

Steininger, Michael, Konstantin Kobs, Padraig Davidson, Anna Krause, and Andreas Hotho. 2021. “Density-Based Weighting for Imbalanced Regression.” Machine Learning 110 (8): 2187–2211. https://doi.org/10.1007/s10994-021-06023-5.

Torgo, Luis, and Rita Ribeiro. 2007. “Utility-Based Regression.” In Knowledge Discovery in Databases: PKDD 2007: 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007. Proceedings 11, 597–604. Springer.

———. 2009. “Precision and Recall for Regression.” In Discovery Science: 12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009 12, 332–46. Springer.

Torgo, Luís, Rita P. Ribeiro, Bernhard Pfahringer, and Paula Branco. 2013. “SMOTE for Regression.” In Progress in Artificial Intelligence, edited by Luís Correia, Luís Paulo Reis, and José Cascalho, 378–89. Berlin, Heidelberg: Springer Berlin Heidelberg.