- S. Kitamori, H. Sakai, and H. Sakaji, “Extraction of sentences concerning business performance forecast and economic forecast from summaries of financial statements by deep learning,” in IEEE Symposium Series on Computational Intelligence, Honolulu, USA, Dec. 2017, pp. 1-7. doi: 10.1109/SSCI.2017.8285335
- N. Antonio, A. de Almeida, and L. Nunes, “Predicting hotel bookings cancellation with a machine learning classification model,” in IEEE International Conference on Machine Learning and Applications, Cancun, Mexico, Dec. 2017, pp. 1049–1054. doi: 10.1109/ICMLA.2017.00-11
- N. Antonio, A. de Almeida, and L. Nunes, “Predicting hotel booking cancellations to decrease uncertainty and increase revenue,” Tourism & Management Studies, vol. 13, no. 2, pp. 25–39, 2017. doi: 10.18089/tms.2017.13203
- L. Rokach, “Decision forest: Twenty years of research,” Information Fusion, vol. 27, pp. 111–125, 2016. doi: 10.1016/j.inffus.2015.06.005
- P. Fernandez-Gonzalez, C. Bielza, and P. Larranaga, “Random forests for regression as a weighted sum of k-potential nearest neighbors,” IEEE Access, vol. 7, pp. 25660–25672, 2019. doi: 10.1109/ACCESS.2019.2900755
- M. C. M. Oo and T. Thein, “Hyperparameters optimization in scalable random forest for big data analytics,” in 4th International Conference on Computer and Communication Systems, Singapore, Singapore, Feb. 2019, pp. 125-129. doi: 10.1109/CCOMS.2019.8821752
- B. H. Shekar and G. Dagnew, “Grid search-based hyperparameter tuning and classification of microarray cancer data,” in International Conference on Advanced Computational and Communication Paradigms, Gantok, India, Feb. 2019, pp. 1-8. doi: 10.1109/ICACCP.2019.8882943
- T. Wang et al., “Random forest-bayesian optimization for product quality prediction with large-scale dimensions in process industrial cyber-physical systems,” IEEE Internet Things Journal, vol. 7, no. 9, pp. 8641-8653, 2020. doi: 10.1109/JIOT.2020.2992811
- E. Hazan, A. Klivans, and Y. Yuan, “Hyperparameter optimization: A spectral approach,” 2017, arXiv:1706.00764
- J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” Journal of Machine Learning Research, vol. 13, pp. 281-305, 2012
- N. Antonio, A. de Almeida, and L. Nunes, “Hotel booking demand datasets,” Data in Brief, vol. 22, pp. 41–49, 2019. doi: 10.1016/j.dib.2018.11.126
- C. Seger, “An investigation of categorical variable encoding techniques in machine learning: binary versus one-hot and feature hashing,” thesis, KTH Royal Insitute of Technology, Stockholm, Sweden, 2018
- J. S. Lee, “AUC4.5: AUC-based C4.5 decision tree algorithm for imbalanced data classification,” IEEE Access, vol. 7, pp. 106034 – 106042, 2019, doi: 10.1109/ACCESS.2019.2931865
- J. Li, S. Ma, T. Le, L. Liu, and J. Liu, “Causal decision trees,” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 2, pp. 257–271, 2017. doi: 10.1109/TKDE.2016.2619350
- T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in International Conference on Knowledge Discovery and Data Mining, New York, USA, Aug. 2016, pp. 785-794. doi: 10.1145/2939672.2939785
- M. Chen, Q. Liu, S. Chen, Y. Liu, C. H. Zhang, and R. Liu, “XGBoost-Based algorithm interpretation and application on post-fault transient stability status prediction of power system,” IEEE Access, vol. 7, pp. 13149-13158, 2019. doi: 10.1109/ACCESS.2019.2893448
- N. Li, B. Li, and L. Gao, “Transient stability assessment of power system based on XGBoost and factorization machine,” IEEE Access, vol. 8, pp. 28403-28414,2020. doi: 10.1109/ACCESS.2020.2969446
- S. Georganos, T. Grippa, S. Vanhuysse, M. Lennert, M. Shimoni, and E. Wolff, “Very high resolution object-based land use-land cover urban classification using extreme gradient boosting,” IEEE Geoscience and Remote Sensing Letter, vol. 15, no. 4, pp. 607-611, 2018. doi: 10.1109/LGRS.2018.2803259
- D. Zhang, L. Qian, B. Mao, C. Huang, B. Huang, and Y. Si, “A data-driven design for fault detection of wind turbines using Random Forests and XGboost,” IEEE Access, vol. 6, pp. 21020–21031, 2018. doi: 10.1109/ACCESS.2018.2818678
- L. Breiman, “Random forests,” Machine Learning, vol. 45, pp. 5–32, 2001. doi: 10.1023/A:1010933404324
- S. Liu, H. Li, Y. Zhang, B. Zou, and J. Zhao, “Random forest-based track initiation method,” Journal of Engineering, vol. 2019, no. 19, pp. 6175-6179, 2019. doi: 10.1049/joe.2019.0180
- A. Primajaya and B. N. Sari, “Random Forest algorithm for prediction of precipitation,” Indonesian Journal of Artificial Intelligence and Data Mining, vol. 1, no. 1, pp. 27, 2018. doi: 10.24014/ijaidm.v1i1.4903
- D. Marinov and D. Karapetyan, “Hyperparameter optimisation with early termination of poor performers,” in Computer Science and Electronic Engineering, Colchester, UK, Sept. 2019, pp. 160–163. doi: 10.1109/CEEC47804.2019.8974317
- B. Nakisa, M. N. Rastgoo, A. Rakotonirainy, F. Maire, and V. Chandran, “Long short term memory hyperparameter optimization for a neural network based emotion recognition framework,” IEEE Access, vol. 6, pp. 49325–49338, 2018. doi: 10.1109/ACCESS.2018.2868361
- M. Feurer and F. Hutter, Hyperparameter Optimization. Springer, 2019. doi: 10.1007/978-3-030-05318-5_1
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