A Synthetic Minority Oversampling Method Based on Local Densities in Low-Dimensional Space for Imbalanced Learning

Zhipeng Xie, Liyang Jiang, Tengju Ye, Xiaoli Li. A Synthetic Minority Oversampling Method Based on Local Densities in Low-Dimensional Space for Imbalanced Learning. In Matthias Renz, Cyrus Shahabi, Xiaofang Zhou, Muhammad Aamir Cheema, editors, Database Systems for Advanced Applications - 20th International Conference, DASFAA 2015, Hanoi, Vietnam, April 20-23, 2015, Proceedings, Part II. Volume 9050 of Lecture Notes in Computer Science, pages 3-18, Springer, 2015. [doi]

@inproceedings{XieJYL15,
  title = {A Synthetic Minority Oversampling Method Based on Local Densities in Low-Dimensional Space for Imbalanced Learning},
  author = {Zhipeng Xie and Liyang Jiang and Tengju Ye and Xiaoli Li},
  year = {2015},
  doi = {10.1007/978-3-319-18123-3_1},
  url = {http://dx.doi.org/10.1007/978-3-319-18123-3_1},
  researchr = {https://researchr.org/publication/XieJYL15},
  cites = {0},
  citedby = {0},
  pages = {3-18},
  booktitle = {Database Systems for Advanced Applications - 20th International Conference, DASFAA 2015, Hanoi, Vietnam, April 20-23, 2015, Proceedings, Part II},
  editor = {Matthias Renz and Cyrus Shahabi and Xiaofang Zhou and Muhammad Aamir Cheema},
  volume = {9050},
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  isbn = {978-3-319-18122-6},
}