1. Benavoli, A., Corani, G., Demšar, J., Zaffalon, M.: Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis. J. Mach. Learn. Res. 18(77), 1–36 (2017)
2. Bergstra, J., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyper-parameter optimization. In: Proceedings of the 24th International Conference on Neural Information Processing Systems. NIPS’11, pp. 2546–2554. Curran Associates Inc., Red Hook (2011)
3. Bergstra, J., Yamins, D., Cox, D.D.: Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. In: Proceedings of the 30th International Conference on International Conference on Machine Learning. ICML’13, vol. 28, pp. 115–123 (2013)
4. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/a:1010933404324
5. Cao, H., Bernard, S., Sabourin, R., Heutte, L.: A novel random forest dissimilarity measure for multi-view learning. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 1344–1351 (2021). https://doi.org/10.1109/ICPR48806.2021.9412961