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Improving AdaBoost Classifier to Predict Enterprise Performance after COVID-19

Author

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  • Jung-Kai Tsai

    (Ph.D. Program in Finance and Banking, National Kaohsiung University of Science and Technology, Kaohsiung City 811532, Taiwan)

  • Chih-Hsing Hung

    (Department of Money and Banking, National Kaohsiung University of Science and Technology, Kaohsiung City 811532, Taiwan)

Abstract

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.

Suggested Citation

  • Jung-Kai Tsai & Chih-Hsing Hung, 2021. "Improving AdaBoost Classifier to Predict Enterprise Performance after COVID-19," Mathematics, MDPI, vol. 9(18), pages 1-10, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2215-:d:632366
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    References listed on IDEAS

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    4. Yi-Chung Hu & Peng Jiang & Ping-Chuan Lee, 2019. "Forecasting tourism demand by incorporating neural networks into Grey–Markov models," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(1), pages 12-20, January.
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    Cited by:

    1. Olivér Hornyák & László Barna Iantovics, 2023. "AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics," Mathematics, MDPI, vol. 11(8), pages 1-24, April.
    2. He Huang & Liwei Zhong & Ting Shen & Huixin Wang, 2022. "Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-SVM model," Journal of Combinatorial Optimization, Springer, vol. 44(5), pages 3778-3791, December.
    3. Pérez-Campuzano, Darío & Rubio Andrada, Luis & Morcillo Ortega, Patricio & López-Lázaro, Antonio, 2022. "Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance," Journal of Air Transport Management, Elsevier, vol. 101(C).

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