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Titlebook: Bankruptcy Prediction through Soft Computing based Deep Learning Technique; Arindam Chaudhuri,Soumya K Ghosh Book 2017 Springer Nature Sin

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樓主
發(fā)表于 2025-3-21 18:05:30 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Bankruptcy Prediction through Soft Computing based Deep Learning Technique
影響因子2023Arindam Chaudhuri,Soumya K Ghosh
視頻videohttp://file.papertrans.cn/181/180751/180751.mp4
發(fā)行地址Highlights the latest research on deep learning integrated with hierarchical Bayesian statistics for bankruptcy prediction..Presents the mathematical framework of the prediction model in a very lucid
圖書封面Titlebook: Bankruptcy Prediction through Soft Computing based Deep Learning Technique;  Arindam Chaudhuri,Soumya K Ghosh Book 2017 Springer Nature Sin
影響因子.This book proposes complex hierarchical deep architectures (HDA) for predicting bankruptcy, a topical issue for business and corporate institutions that in the past has been tackled using statistical, market-based and machine-intelligence prediction models. The HDA are formed through fuzzy rough tensor deep staking networks (FRTDSN) with structured, hierarchical rough Bayesian (HRB) models. FRTDSN is formalized through TDSN and fuzzy rough sets, and HRB is formed by incorporating probabilistic rough sets in structured hierarchical Bayesian model. Then FRTDSN is integrated with HRB to form the compound FRTDSN-HRB model. HRB enhances the prediction accuracy of FRTDSN-HRB model. The experimental datasets are adopted from Korean construction companies and American and European non-financial companies, and the research presented focuses on the impact of choice of cut-off points, sampling procedures and business cycle on the accuracy of bankruptcy prediction models.. .The bookalso highlights the fact that misclassification can result in erroneous predictions leading to prohibitive costs to investors and the economy, and shows that choice of cut-off point and sampling procedures affect r
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沙發(fā)
發(fā)表于 2025-3-21 20:28:56 | 只看該作者
板凳
發(fā)表于 2025-3-22 00:42:46 | 只看該作者
Literature Review,univariate analysis in order to predict corporate bankruptcy. Neter [5] however strongly supported the use of multivariate analysis for this problem. Beaver discovered that till 5-year period before the organization’s failure, the number of ratios differed from corresponding non-failed organization.
地板
發(fā)表于 2025-3-22 08:38:20 | 只看該作者
Bankruptcy Prediction,the number of business failures. There are four generic terms that describe corporate distress, viz., failure, insolvency, default, and bankruptcy, which form the backbone of corporate distress which ultimately leads to bankruptcy:
5#
發(fā)表于 2025-3-22 12:12:38 | 只看該作者
Need for Risk Classification,from the banks. Here no external rating is available. The banks use internal rating system to sketch the risk class to which the client belongs. The banks are given pressure from both sides by the Basel II. The risk premia is demanded by the banks with respect to the default probability of the speci
6#
發(fā)表于 2025-3-22 16:19:32 | 只看該作者
Conclusion,kruptcy database. FRTDSN-HRB performance is compared with fuzzy SVMs and other statistical models. To provide a balance in comparison, certain aspects that reduce or grow predictive accuracy are taken care through FRTDSN-HRB model. The cutoff points’ selection is affected by backcasting, choice-base
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發(fā)表于 2025-3-22 19:27:02 | 只看該作者
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發(fā)表于 2025-3-22 23:50:49 | 只看該作者
Bankruptcy Prediction through Soft Computing based Deep Learning Technique978-981-10-6683-2
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發(fā)表于 2025-3-23 01:24:44 | 只看該作者
https://doi.org/10.1057/9780230612082s achieved through literature review and brainstorming the techniques. The literature review is presented in Chap. .. The methodology is highlighted in Chap. .. The appropriate data is collected to validate the chosen technique. The data preprocessing is performed with respect to the chosen techniqu
10#
發(fā)表于 2025-3-23 08:04:43 | 只看該作者
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