1 |
Saez, J. A., Luengo, J., Stefanowski, J., & Herrera, F. (2015). SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Information Sciences, 291, 184-203.
DOI
|
2 |
Wu, G., & Chang, E. Y. (2003, August). Class-boundary alignment for imbalanced dataset learning. In ICML 2003 workshop on learning from imbalanced data sets II, Washington, DC (pp. 49-56).
|
3 |
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.
DOI
|
4 |
Cheng, K., Zhang, C., Yu, H., Yang, X., Zou, H., & Gao, S. (2019). Grouped SMOTE with noise filtering mechanism for classifying imbalanced data. IEEE Access, 7, 170668-170681.
DOI
|
5 |
Ghorbani, R., & Ghousi, R. (2020). Comparing different resampling methods in predicting students' performance using machine learning techniques. IEEE Access, 8, 67899-67911.
DOI
|
6 |
Cortez, P., & Silva, A. M. G. (2008). Using data mining to predict secondary school student performance.
|
7 |
Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. in: Proceedings of Second International Conference on Knowledge Discovery and Data Mining, Portland, OR, 226-231.
|
8 |
Gazzah, S., & Amara, N. E. B. (2008, September). New oversampling approaches based on polynomial fitting for imbalanced data sets. In 2008 the eighth iapr international workshop on document analysis systems (pp. 677-684). IEEE.
|
9 |
Krawczyk, B. (2016). Learning from imbalanced data: open challenges and future directions. Progress in Artificial Intelligence, 5(4), 221-232.
DOI
|
10 |
Lee, D., & Kim, N. (2022). Anomaly Detection Methodology Based on Multimodal Deep Learning. Journal of Intelligence and Information Systems, 28(2), 101-125.
DOI
|
11 |
Choi, N., & Kim, W. (2019). Anomaly Detection for User Action with Generative Adversarial Networks. Journal of Intelligence and Information Systems, 25(3), 43-62.
DOI
|
12 |
Fernandez, A., Garcia, S., Herrera, F., & Chawla, N. V. (2018). SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. Journal of artificial intelligence research, 61, 863-905.
DOI
|
13 |
Han, H., Wang, W. Y., & Mao, B. H. (2005, August). Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In International conference on intelligent computing (pp. 878-887). Springer, Berlin, Heidelberg.
|
14 |
Nguyen, H. D., Tran, K. P., Thomassey, S., & Hamad, M. (2021). Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management. International Journal of Information Management, 57, 102282.
DOI
|
15 |
Shin, B., Lee, J., Han, S., & Park, C.-S. (2021). A Study of Anomaly Detection for ICT Infrastructure using Conditional Multimodal Autoencoder. Journal of Intelligence and Information Systems, 27(3), 57-73.
DOI
|
16 |
Ali, H., Salleh, M. N. M., Saedudin, R., Hussain, K., & Mushtaq, M. F. (2019). Imbalance class problems in data mining: a review. Indonesian Journal of Electrical Engineering and Computer Science, 14(3), 1560-1571.
|
17 |
Serradilla, O., Zugasti, E., Ramirez de Okariz, J., Rodriguez, J., & Zurutuza, U. (2021). Adaptable and explainable predictive maintenance: Semi-supervised deep learning for anomaly detection and diagnosis in press machine data. Applied Sciences, 11(16), 7376.
DOI
|