To cite sansa in publications use:

Machine learning is widely used in information-systems design. Yet, training algorithms on imbalanced datasets may severely affect performance on unseen data. For example, in some cases in healthcare, fintech, or cybersecurity contexts, certain subclasses are difficult to learn because they are underrepresented in training data. This R package offers a flexible and efficient solution based on a new synthetic average neighborhood sampling algorithm (SANSA), which, in contrast to other solutions, introduces a novel “placement” parameter that can be tuned to adapt to each dataset’s unique manifestation of the imbalance.

Corresponding BibTeX entry:

  @Article{,
    title = {Improving Imbalanced Machine Learning with
      Neighborhood-Informed Synthetic Sample Placement},
    author = {Murtaza Nasir and Ali Dag and Serhat Simsek and Anton
      Ivanov and Asil Oztekin},
    journal = {Journal of Management Information Systems},
    year = {2022},
    volume = {39},
    number = {3},
    pages = {20},
    url = {https://www.jmis-web.org/},
  }