Ensemble learning (original) (raw)

1 Zhi-Hua Zhou. Ensemble Methods: Foundations and Algorithms. CRC Press. 2012.

2 Gavin Brown. “Ensemble Learning”. Encyclopedia of Machine Learning and Data Mining. Springer. 2017.

3 Gautam Kunapuli. Ensemble Methods for Machine Learning. Manning Publications. 2023. Lior Rokach. Pattern Classification Using Ensemble Methods. World Scientific Publishing Company. 2010.

4 Zhi-Hua Zhou. Ensemble Methods: Foundations and Algorithms. CRC Press. 2012.

5 Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor. An Introduction to Statistical Learning with Applications in Python. Springer. 2023.

6 George Kyriakides and Konstantinos G. Margaritis. Hands-On Ensemble Learning with Python. Packt Publishing. 2019.

7 Zhi-Hua Zhou. Machine Learning, translated by Shaowu Liu. Springer. 2021. George Kyriakides and Konstantinos G. Margaritis. Hands-On Ensemble Learning with Python. Packt Publishing. 2019.

8 Peter Sollich and Anders Krogh. “Learning with Ensembles: How Overfitting Can Be Useful”. Advances in Neural Information Processing Systems. Vol. 8. 1995. https://papers.nips.cc/paper_files/paper/1995/hash/1019c8091693ef5c5f55970346633f92-Abstract.html.

9 Gautam Kunapuli. Ensemble Methods for Machine Learning. Manning Publications. 2023.

10 Zhi-Hua Zhou. Ensemble Methods: Foundations and Algorithms. CRC Press. 2012.

11 Ibomoiye Domor Mienye and Yanxia Sun. “A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects”. IEEE Access. Vol. 10. 2022. pp. 99129–99149. https://ieeexplore.ieee.org/document/9893798. Lior Rokach. “Ensemble-based Classifiers”. Artificial Intelligence Review. Vol. 33. 2010. pp. 1–39. https://link.springer.com/article/10.1007/s10462-009-9124-7.

12 M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, and F. Herrera. “A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches”. IEEE Transactions on Systems, Man, and Cybernetics. Vol. 42. No. 4. 2012. pp. 463–484. https://ieeexplore.ieee.org/document/5978225.

13 Zhi-Hua Zhou. Ensemble Methods: Foundations and Algorithms. CRC Press. 2012.

14 Gautam Kunapuli. Ensemble Methods for Machine Learning. Manning Publications. 2023.

15 Robi Palikar. “Ensemble Learning”. Ensemble Machine Learning: Methods and Applications. Springer. 2012.

16 Ibomoiye Domor Mienye and Yanxia Sun. “A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects”. IEEE Access. Vol. 10. 2022. pp. 99129–99149. https://ieeexplore.ieee.org/document/9893798.

17 Zhi-Hua Zhou. Ensemble Methods: Foundations and Algorithms. CRC Press. 2012. Gautam Kunapuli. Ensemble Methods for Machine Learning. Manning Publications. 2023.

18 Robi Palikar. “Ensemble Learning”. Ensemble Machine Learning: Methods and Applications. Springer. 2012. Zhi-Hua Zhou. Ensemble Methods: Foundations and Algorithms. CRC Press. 2012.

19 Gautam Kunapuli. Ensemble Methods for Machine Learning. Manning Publications. 2023.

20 Devesh Walawalkar, Zhiqiang Shen, and Marios Savvides. “Online Ensemble Model Compression Using Knowledge Distillation”. 2020. pp. 18–35. https://link.springer.com/chapter/10.1007/978-3-030-58529-7_2.

21 Xinzhe Han, Shuhui Wang, Chi Su, Qingming Huang, and Qi Tian. “Greedy Gradient Ensemble for Robust Visual Question Answering”. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 2021. pp. 1584–1593. https://openaccess.thecvf.com/content/ICCV2021/html/Han_Greedy_Gradient_Ensemble_for_Robust_Visual_Question_Answering_ICCV_2021_paper.html.

22 Usman Gohar, Sumon Biswas, and Hridesh Rajan. “Towards Understanding Fairness and Its Composition in Ensemble Machine Learning”. 45th IEEE/ACM International Conference on Software Engineering (ICSE). 2023. pp. 1533–1545. https://ieeexplore.ieee.org/abstract/document/10172501. Khaled Badran, Pierre-Olivier Côté, Amanda Kolopanis, Rached Bouchoucha, Antonio Collante, Diego Elias Costa, Emad Shihab, and Foutse Khomh. “Can Ensembling Preprocessing Algorithms Lead to Better Machine Learning Fairness?” Computer. Vol. 56. No. 4. 2023. pp. 71–79. https://ieeexplore.ieee.org/abstract/document/10098174. Swanand Kadhe, Anisa Halimi, Ambrish Rawat, and Nathalie Baracaldo. “FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs”. Socially Responsible Language Modelling Research Workshop. 2023. https://neurips.cc/virtual/2023/78908.