Author:
Ganepola Dasuni,Karunaratne Indika,Maduranga M. W. P.
Publisher
Springer Nature Switzerland
Reference8 articles.
1. Krishnapuram, B., Yu, S., Rao, B.: Cost-Sensitive Machine Learning. CRC Press, Boca Raton (2012)
2. Mienye, I.D., Sun, Y.: Performance analysis of cost-sensitive learning methods with application to imbalanced medical data. Inform. Med. Unlocked 25, 100690 (2021). https://doi.org/10.1016/j.imu.2021.100690
3. Narassiguin, A.: Ensemble learning, comparative analysis and further improvements with dynamic ensemble selection. Artificial Intelligence [cs.AI]. Université de Lyon (2018). English. ffNNT: 2018LYSE1075ff. fftel-02146962f
4. Lodge, J.M., et al.: Understanding difficulties and resulting confusion in learning: an integrative review. Front. Educ. 3 (2018). https://doi.org/10.3389/feduc.2018.00049
5. Dong, S.-Q., et al.: How to improve machine learning models for lithofacies identification by practical and novel ensemble strategy and principles. Pet. Sci. 20(2), 733–752 (2023)