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Entropy
Volume 24
Issue 5
10.3390/e24050617
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Article

Random RotBoost: An Ensemble Classification Method Based on Rotation Forest and AdaBoost in Random Subsets and Its Application to Clinical Decision Support

by
Shin-Jye Lee
1,
Ching-Hsun Tseng
2,
Hui-Yu Yang
1,
Xin Jin
3,
Qian Jiang
3,
Bin Pu
4,
Wei-Huan Hu
5,
Duen-Ren Liu
6,
Yang Huang
6 and
Na Zhao
3,*
1
Institute of Management of Technology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
2
Department of Computer Science, The University of Manchester, Manchester M13 9PL, UK
3
National Pilot School of Software, Yunnan University, Kunming 650504, China
4
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
5
College of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
6
Institute of Information Management, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
*
Author to whom correspondence should be addressed.
Entropy 2022, 24(5), 617; https://doi.org/10.3390/e24050617
Submission received: 8 March 2022 / Revised: 1 April 2022 / Accepted: 19 April 2022 / Published: 28 April 2022
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Abstract

In the era of bathing in big data, it is common to see enormous amounts of data generated daily. As for the medical industry, not only could we collect a large amount of data, but also see each data set with a great number of features. When the number of features is ramping up, a common dilemma is adding computational cost during inferring. To address this concern, the data rotational method by PCA in tree-based methods shows a path. This work tries to enhance this path by proposing an ensemble classification method with an AdaBoost mechanism in random, automatically generating rotation subsets termed Random RotBoost. The random rotation process has replaced the manual pre-defined number of subset features (free pre-defined process). Therefore, with the ensemble of the multiple AdaBoost-based classifier, overfitting problems can be avoided, thus reinforcing the robustness. In our experiments with real-world medical data sets, Random RotBoost reaches better classification performance when compared with existing methods. Thus, with the help from our proposed method, the quality of clinical decisions can potentially be enhanced and supported in medical tasks.
Keywords: classification; Rotation Forest; AdaBoost; clinical decision support classification; Rotation Forest; AdaBoost; clinical decision support

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MDPI and ACS Style

Lee, S.-J.; Tseng, C.-H.; Yang, H.-Y.; Jin, X.; Jiang, Q.; Pu, B.; Hu, W.-H.; Liu, D.-R.; Huang, Y.; Zhao, N. Random RotBoost: An Ensemble Classification Method Based on Rotation Forest and AdaBoost in Random Subsets and Its Application to Clinical Decision Support. Entropy 2022, 24, 617. https://doi.org/10.3390/e24050617

AMA Style

Lee S-J, Tseng C-H, Yang H-Y, Jin X, Jiang Q, Pu B, Hu W-H, Liu D-R, Huang Y, Zhao N. Random RotBoost: An Ensemble Classification Method Based on Rotation Forest and AdaBoost in Random Subsets and Its Application to Clinical Decision Support. Entropy. 2022; 24(5):617. https://doi.org/10.3390/e24050617

Chicago/Turabian Style

Lee, Shin-Jye, Ching-Hsun Tseng, Hui-Yu Yang, Xin Jin, Qian Jiang, Bin Pu, Wei-Huan Hu, Duen-Ren Liu, Yang Huang, and Na Zhao. 2022. "Random RotBoost: An Ensemble Classification Method Based on Rotation Forest and AdaBoost in Random Subsets and Its Application to Clinical Decision Support" Entropy 24, no. 5: 617. https://doi.org/10.3390/e24050617

APA Style

Lee, S.-J., Tseng, C.-H., Yang, H.-Y., Jin, X., Jiang, Q., Pu, B., Hu, W.-H., Liu, D.-R., Huang, Y., & Zhao, N. (2022). Random RotBoost: An Ensemble Classification Method Based on Rotation Forest and AdaBoost in Random Subsets and Its Application to Clinical Decision Support. Entropy, 24(5), 617. https://doi.org/10.3390/e24050617

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