MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation

By Francisco Charte, Antonio Jesús Rivera, M. J. Jesús, Francisco Herrera. 2015

246 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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. Francisco Herrera
2. Alberto Fernández
3. Salvador Garcia
4. Julián Luengo
5. Luís Torgo
6. Paula Branco
7. Rita P. Ribeiro
8. José Manuel Benítez

In Computer Vision and Pattern Recognition

1. Salvador Garcia
2. Julián Luengo
3. Francisco Herrera
4. Jason Wei
5. Sarath Chandar
6. Sergio Ramírez-Gallego
7. Alberto Fernández
8. José Manuel Benítez

In Evaluation & Benchmarks

1. Francisco Herrera
2. Alberto Fernández
3. Salvador Garcia
4. Jason Wei
5. Sarath Chandar
6. Nitesh V. Chawla
7. Eduard H. Hovy
8. Paula Branco

Citations in the corpus

• A Survey of Data Augmentation Approaches for NLP (2021, methodology): “M ULTILABEL SMOTE (MLSMOTE) (Charte et al., 2015) modifies SMOTE to balance classes for multi-label classification, where classi-fiers predict more than one class at the same time.”
• COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios (2020, unknown): “To answer this question, i.e., define the best feature representation, we have used the statistical protocol proposed in Charte et al. [69].”
• Big data preprocessing: methods and prospects (2016, methodology): “Thus, methods for re-sampling these particular data sets have been proposed [52, 53].”
• Learning from imbalanced data: open challenges and future directions (2016, unknown): “In multi-label learning so far measures of imbalance and SMOTE-based oversampling approach have been proposed [8].”
• SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary (2018, methodology): “MLSMOTE (Charte et al., 2015) is the most popular extension of SMOTE designed for multilabel classification.”
• A Survey of Predictive Modeling on Imbalanced Domains (2016, background): “…this problem within other contexts such as regression [Torgo et al. 2013], ordinal classification [P´erez-Ortiz et al. 2014], multi-label classification [Charte et al. 2015b], association rules mining [Luna et al. 2015], multi-instance learning [Wang et al. 2013b], and data streams [Wang and Abraham…”