04983nam 22007095 450 991025484370332120200703010555.0981-10-6683-310.1007/978-981-10-6683-2(CKB)4100000001381908(DE-He213)978-981-10-6683-2(MiAaPQ)EBC5178310(PPN)222226544(EXLCZ)99410000000138190820171201d2017 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierBankruptcy Prediction through Soft Computing based Deep Learning Technique /by Arindam Chaudhuri, Soumya K Ghosh1st ed. 2017.Singapore :Springer Singapore :Imprint: Springer,2017.1 online resource (XVII, 102 p. 59 illus.) 981-10-6682-5 Includes bibliographical references.Introduction -- Need of this Research -- Literature Review -- Bankruptcy Prediction Methodology -- Need for Risk Classification -- Experimental Framework: Bankruptcy Prediction using Soft Computing based Deep Learning Technique.- Datasets Used -- Experimental Results -- Conclusion .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.User interfaces (Computer systems)Artificial intelligenceComputer simulationManagement information systemsComputer scienceBanks and bankingStatisticsUser Interfaces and Human Computer Interactionhttps://scigraph.springernature.com/ontologies/product-market-codes/I18067Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Simulation and Modelinghttps://scigraph.springernature.com/ontologies/product-market-codes/I19000Management of Computing and Information Systemshttps://scigraph.springernature.com/ontologies/product-market-codes/I24067Bankinghttps://scigraph.springernature.com/ontologies/product-market-codes/626010Statistics for Business, Management, Economics, Finance, Insurancehttps://scigraph.springernature.com/ontologies/product-market-codes/S17010User interfaces (Computer systems)Artificial intelligence.Computer simulation.Management information systems.Computer science.Banks and banking.Statistics.User Interfaces and Human Computer Interaction.Artificial Intelligence.Simulation and Modeling.Management of Computing and Information Systems.Banking.Statistics for Business, Management, Economics, Finance, Insurance.005.4374.019Chaudhuri Arindamauthttp://id.loc.gov/vocabulary/relators/aut763017Ghosh Soumya Kauthttp://id.loc.gov/vocabulary/relators/autBOOK9910254843703321Bankruptcy Prediction through Soft Computing based Deep Learning Technique2500581UNINA