SMOTE for Regression

By Luís Torgo, Rita P. Ribeiro, Bernhard Pfahringer, Paula Branco. Portuguese Conference on Artificial Intelligence, 2013

308 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/24a21b58977e5496e1d1245ff7a9f60d0cb69870

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Paula Branco
2. Rita P. Ribeiro
3. Luís Torgo
4. Lukas Hubert Leufen
5. Clara Betancourt
6. Amirpasha Mozaffari
7. Rohitash Chandra
8. Michael Langguth

In Information Systems

1. Paula Branco
2. Rohitash Chandra
3. Azal Ahmad Khan
4. Rita P. Ribeiro
5. Omkar Chaudhari
6. Luís Torgo
7. Hadeer Nabil
8. Walid Maalej

Citations in the corpus

• Can deep learning beat numerical weather prediction? (2021, background): “[104]), there appears to be little research on dealing with imbalanced sample sizes in regression problems [105].”
• A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, methodology): “Although SMOTE was developed for classification problems, it can also be extended to regression problems [210].”
• Delving into Deep Imbalanced Regression (2021, unknown): “• Synthetic samples: We choose existing methods for imbalanced regression, including SMOTER (Torgo et al., 2013) and SMOGN (Branco et al.”
• A Survey of Predictive Modeling on Imbalanced Domains (2016, methodology): “This is the case of regression tasks (e.g., Torgo et al. [2013]), multi-class tasks (e.g., Alejo et al. [2014] and Fern´andez-Baldera et al. [2015]), learning from data streams (e.g., Ghazikhani et al. [2014] and Wang and Abraham [2015]), ordinal target variables (e.g., Baccianella et al. [2009],…”