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Your search keyword '"Kaisar, Shahriar"' showing total 28 results
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28 results on '"Kaisar, Shahriar"'

2. Selection of Cloud Service Providers: A Fuzzy-set Qualitative Comparative Analysis Approach

Author

Hossain, Mohammad Alamgir, Sabani, Alvedi, Lokuge, Sachithra, Boo, Yee Ling, Kaisar, Shahriar, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Sharma, Sujeet K., editor, Dwivedi, Yogesh K., editor, Metri, Bhimaraya, editor, Lal, Banita, editor, and Elbanna, Amany, editor

Published
2024
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16. Bankruptcy prediction for imbalanced dataset using oversampling and ensemble machine learning methods.

Author

Chowdhury, Abdullahi, Kaisar, Shahriar, and Naha, Ranesh

Subjects
*BANKRUPTCY, *FINANCIAL ratios, *INVESTORS, *MACHINE learning, *FINANCIAL statements, *MACHINE performance
Abstract

Business owners, investors, governments, banks, securities, and other financial participants have increasingly relied on bankruptcy prediction as a way to protect their assets. Managers and investors can make informed decisions about economic strategies by analysing data such as financial position, cash flows, balance sheets, income statements and other financial ratios available in financial statements. A thorough investigation into how the presence of samples of varying natures in the positive class may affect the predictive performance of machine learning models could be instructive, given the significance of incorrectly identifying default or bankrupt cases. In this study, we used an imbalanced dataset of Polish businesses to predict bankruptcy and investigated the performance of oversampling techniques combined with ensemble machine learning models to improve forecasting accuracy. According to simulation results, our approach achieved 99% accuracy and outperformed existing approaches in the literature. [ABSTRACT FROM AUTHOR]

Published
2023
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