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논문 리스트

논문 리스트

1. Imbalanced Data

1-1. Done

Title Year Arthor Journal ETC.
Geometric SMOTE a geometrically enhanced drop-in replacement for SMOTE 2019 Douzas, G., & Bacao, F. Information Sciences 참고
Smote for regession 2013 Torgo, L., Ribeiro, R. P., Pfahringer, B., & Branco, P. Progress in Artificial Intelligence.
Delving into deep imbalanced regression 2021 Yang, Y., Zha, K., Chen, Y., Wang, H., & Katabi, D. PMLR ICML
A random forests quantile classifier for class imbalanced data 2019 O’Brien, R., & Ishwaran, H. Pattern recognition
Bayesian Imbalanced Regression Debiasing 2021 Ren, J., Zhang, M., Yu, C., & Liu, Z.
Utility-based regression 2007 Torgo, L., & Ribeiro, R. ECML PKDD
  1. Douzas, G., & Bacao, F. (2019). Geometric SMOTE a geometrically enhanced drop-in replacement for SMOTE. Information Sciences, 501, 118-135.
  2. Torgo, L., Ribeiro, R.P., Pfahringer, B., Branco, P. (2013). SMOTE for Regression. In: Correia, L., Reis, L.P., Cascalho, J. (eds) Progress in Artificial Intelligence. EPIA 2013. Lecture Notes in Computer Science(), vol 8154. Springer, Berlin, Heidelberg.
  3. Yang, Y., Zha, K., Chen, Y., Wang, H., & Katabi, D. (2021, July). Delving into deep imbalanced regression. In International Conference on Machine Learning (pp. 11842-11851). PMLR.
  4. O’Brien, R., & Ishwaran, H. (2019). A random forests quantile classifier for class imbalanced data. Pattern recognition, 90, 232-249.
  5. Ren, J., Zhang, M., Yu, C., & Liu, Z. (2021). Bayesian Imbalanced Regression Debiasing.
  6. Torgo, L., & Ribeiro, R. (2007, September). Utility-based regression. In European conference on principles of data mining and knowledge discovery (pp. 597-604). Springer, Berlin, Heidelberg.

1-2. To Read

  1. Camacho, L., Douzas, G., & Bacao, F. (2022). Geometric SMOTE for regression. Expert Systems with Applications, 116387.
  2. Chen, B., Xia, S., Chen, Z., Wang, B., & Wang, G. (2021). RSMOTE: A self-adaptive robust SMOTE for imbalanced problems with label noise. Information Sciences, 553, 397-428.
  3. Kamycki, K., Kapuscinski, T., & Oszust, M. (2019). Data augmentation with suboptimal warping for time-series classification. Sensors, 20(1), 98.
  4. Dablain, D., Krawczyk, B., & Chawla, N. V. (2022). DeepSMOTE: Fusing deep learning and SMOTE for imbalanced data. IEEE Transactions on Neural Networks and Learning Systems.
  5. Branco, P., Torgo, L., & Ribeiro, R. P. (2017, October). SMOGN: a pre-processing approach for imbalanced regression. In First international workshop on learning with imbalanced domains: Theory and applications (pp. 36-50). PMLR.
  6. Branco, P., Torgo, L., & Ribeiro, R. P. (2019). Pre-processing approaches for imbalanced distributions in regression. Neurocomputing, 343, 76-99.
  7. Xia, S., Zheng, Y., Wang, G., He, P., Li, H., & Chen, Z. (2021). Random space division sampling for label-noisy classification or imbalanced classification. IEEE Transactions on Cybernetics.

2. Anomaly Detection

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