Using an ensemble classifier based on sequential floating forward selection for financial distress prediction problem
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DOI: 10.1016/j.jretconser.2016.10.002
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Cited by:
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- Xiaobo Tang & Shixuan Li & Mingliang Tan & Wenxuan Shi, 2020. "Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 769-787, August.
- Rémi Stellian & Gabriel I. Penagos & Jenny P. Danna-Buitrago, 2021. "Firms in financial distress: evidence from inter-firm payment networks with volatility driven by ‘animal spirits’," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(1), pages 59-101, January.
- Ahmad Hammami & Mohammad Hendijani Zadeh, 2022. "Predicting earnings management through machine learning ensemble classifiers," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1639-1660, December.
- Liang, Deron & Tsai, Chih-Fong & Lu, Hung-Yuan (Richard) & Chang, Li-Shin, 2020. "Combining corporate governance indicators with stacking ensembles for financial distress prediction," Journal of Business Research, Elsevier, vol. 120(C), pages 137-146.
- Marina Eni & Valeria Mordoh & Yaniv Zigel, 2022. "Cough detection using a non-contact microphone: A nocturnal cough study," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-22, January.
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Keywords
Bankruptcy prediction problem; Sequential floating forward; Artificial bee colony; Support vector machine; Genetic algorithm;All these keywords.
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