References#

Research Papers#

ET-COME Method Papers: - ET-COME: Equilibrium Transport with Conformal Minority Expansion

  • Submitted to NeurIPS 2026

Related Work:

  • Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357.

  • Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123-140.

  • He, H., & Garcia, E. A. (2009). Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263-1284.

External Resources#

Repository: - Main Repository: Pawlo77/ET-COME - Issue Tracker: Pawlo77/ET-COME#issues - Discussions: Pawlo77/ET-COME#discussions

Package: - PyPI: https://pypi.org/project/et-come/

Related Projects: - scikit-learn: https://scikit-learn.org/ - imbalanced-learn: https://imbalanced-learn.org/

Datasets Used in Experiments#

Experiments utilize publicly available datasets from: - UCI Machine Learning Repository - Kaggle - scikit-learn built-in datasets

For detailed dataset descriptions and download instructions, please refer to the experiments documentation in the repository.