Next Article in Journal
Degree Reduction of Q-Bézier Curves via Squirrel Search Algorithm
Previous Article in Journal
Mathematical Modelling of Glioblastomas Invasion within the Brain: A 3D Multi-Scale Moving-Boundary Approach
Article

Improving AdaBoost Classifier to Predict Enterprise Performance after COVID-19

1
Ph.D. Program in Finance and Banking, National Kaohsiung University of Science and Technology, Kaohsiung City 811532, Taiwan
2
Department of Money and Banking, National Kaohsiung University of Science and Technology, Kaohsiung City 811532, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Massimiliano Ferrara
Mathematics 2021, 9(18), 2215; https://doi.org/10.3390/math9182215
Received: 2 June 2021 / Revised: 30 August 2021 / Accepted: 1 September 2021 / Published: 9 September 2021
Because COVID-19 occurred in 2019, the behavioxr of humans has been changed and it will influence the business model of enterprise. Enterprise cannot predict its development according to past knowledge and experiment; so, it needs a new machine learning framework to predict enterprise performance. The goal of this research is to modify AdaBoost to reasonably predict the enterprise performance. In order to justify the usefulness of the proposed model, enterprise data will be collected and the proposed model can be used to predict the enterprise performance after COVID-19. The test data correct rate of the proposed model will be compared with some of the traditional machine learning models. Compared with the traditional AdaBoost, back propagation neural network (BPNN), regression classifier, support vector machine (SVM) and support vector regression (SVR), the proposed method possesses the better classification ability (average correct rate of the proposed method is 88.04%) in handling two classification problems. Compared with traditional AdaBoost, one-against-all SVM, one-against-one SVM, one-against-all SVR and one-against-one SVR, the classification ability of the proposed method is also relatively better for coping with the multi-class classification problem. Finally, some conclusions and future research will be discussed at the end. View Full-Text
Keywords: enterprise performance; machine learning; AdaBoost; COVID-19 enterprise performance; machine learning; AdaBoost; COVID-19
Show Figures

Figure 1

MDPI and ACS Style

Tsai, J.-K.; Hung, C.-H. Improving AdaBoost Classifier to Predict Enterprise Performance after COVID-19. Mathematics 2021, 9, 2215. https://doi.org/10.3390/math9182215

AMA Style

Tsai J-K, Hung C-H. Improving AdaBoost Classifier to Predict Enterprise Performance after COVID-19. Mathematics. 2021; 9(18):2215. https://doi.org/10.3390/math9182215

Chicago/Turabian Style

Tsai, Jung-Kai, and Chih-Hsing Hung. 2021. "Improving AdaBoost Classifier to Predict Enterprise Performance after COVID-19" Mathematics 9, no. 18: 2215. https://doi.org/10.3390/math9182215

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop