Bankruptcy Prediction through Soft Computing based Deep Learning Technique

Nonfiction, Computers, Advanced Computing, Programming, User Interfaces, Artificial Intelligence, General Computing
Cover of the book Bankruptcy Prediction through Soft Computing based Deep Learning Technique by Soumya K Ghosh, Arindam Chaudhuri, Springer Singapore
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Soumya K Ghosh, Arindam Chaudhuri ISBN: 9789811066832
Publisher: Springer Singapore Publication: December 1, 2017
Imprint: Springer Language: English
Author: Soumya K Ghosh, Arindam Chaudhuri
ISBN: 9789811066832
Publisher: Springer Singapore
Publication: December 1, 2017
Imprint: Springer
Language: English

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 book also 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 rankings of various models. It also suggests that empirical cut-off points estimated from training samples result in the lowest misclassification costs for all the models. The book confirms that FRTDSN-HRB achieves superior performance compared to other statistical and soft-computing models. The experimental results are given in terms of several important statistical parameters revolving different business cycles and sub-cycles for the datasets considered and are of immense benefit to researchers working in this area.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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 book also 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 rankings of various models. It also suggests that empirical cut-off points estimated from training samples result in the lowest misclassification costs for all the models. The book confirms that FRTDSN-HRB achieves superior performance compared to other statistical and soft-computing models. The experimental results are given in terms of several important statistical parameters revolving different business cycles and sub-cycles for the datasets considered and are of immense benefit to researchers working in this area.

More books from Springer Singapore

Cover of the book Prospects of Alternative Transportation Fuels by Soumya K Ghosh, Arindam Chaudhuri
Cover of the book Economic Consequence Analysis of Disasters by Soumya K Ghosh, Arindam Chaudhuri
Cover of the book A Study on Antimicrobial Effects of Nanosilver for Drinking Water Disinfection by Soumya K Ghosh, Arindam Chaudhuri
Cover of the book International Students in French Universities and Grandes Écoles: A Comparative Study by Soumya K Ghosh, Arindam Chaudhuri
Cover of the book Taiwan Cinema, Memory, and Modernity by Soumya K Ghosh, Arindam Chaudhuri
Cover of the book Regenerative Medicine: Laboratory to Clinic by Soumya K Ghosh, Arindam Chaudhuri
Cover of the book Trade and the Labor Market by Soumya K Ghosh, Arindam Chaudhuri
Cover of the book China’s Macroeconomic Outlook by Soumya K Ghosh, Arindam Chaudhuri
Cover of the book Dysphagia Evaluation and Treatment by Soumya K Ghosh, Arindam Chaudhuri
Cover of the book The Role of Water in ATP Hydrolysis Energy Transduction by Protein Machinery by Soumya K Ghosh, Arindam Chaudhuri
Cover of the book Data Science and Analytics by Soumya K Ghosh, Arindam Chaudhuri
Cover of the book Dialogues with Social Robots by Soumya K Ghosh, Arindam Chaudhuri
Cover of the book Exploring Innovative Pedagogy in the Teaching and Learning of Chinese as a Foreign Language by Soumya K Ghosh, Arindam Chaudhuri
Cover of the book Global Well-posedness and Asymptotic Behavior of the Solutions to Non-classical Thermo(visco)elastic Models by Soumya K Ghosh, Arindam Chaudhuri
Cover of the book Environmental History and Tribals in Modern India by Soumya K Ghosh, Arindam Chaudhuri
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy