LEADER 05015nam 22007095 450 001 9910254843703321 005 20200703010555.0 010 $a981-10-6683-3 024 7 $a10.1007/978-981-10-6683-2 035 $a(CKB)4100000001381908 035 $a(DE-He213)978-981-10-6683-2 035 $a(MiAaPQ)EBC5178310 035 $a(PPN)222226544 035 $a(EXLCZ)994100000001381908 100 $a20171201d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBankruptcy Prediction through Soft Computing based Deep Learning Technique$b[electronic resource] /$fby Arindam Chaudhuri, Soumya K Ghosh 205 $a1st ed. 2017. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2017. 215 $a1 online resource (XVII, 102 p. 59 illus.) 311 $a981-10-6682-5 320 $aIncludes bibliographical references. 327 $aIntroduction -- 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 . 330 $aThis 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. 606 $aUser interfaces (Computer systems) 606 $aArtificial intelligence 606 $aComputer simulation 606 $aManagement information systems 606 $aComputer science 606 $aBanks and banking 606 $aStatistics  606 $aUser Interfaces and Human Computer Interaction$3https://scigraph.springernature.com/ontologies/product-market-codes/I18067 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 606 $aManagement of Computing and Information Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/I24067 606 $aBanking$3https://scigraph.springernature.com/ontologies/product-market-codes/626010 606 $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010 615 0$aUser interfaces (Computer systems). 615 0$aArtificial intelligence. 615 0$aComputer simulation. 615 0$aManagement information systems. 615 0$aComputer science. 615 0$aBanks and banking. 615 0$aStatistics . 615 14$aUser Interfaces and Human Computer Interaction. 615 24$aArtificial Intelligence. 615 24$aSimulation and Modeling. 615 24$aManagement of Computing and Information Systems. 615 24$aBanking. 615 24$aStatistics for Business, Management, Economics, Finance, Insurance. 676 $a005.437 676 $a4.019 700 $aChaudhuri$b Arindam$4aut$4http://id.loc.gov/vocabulary/relators/aut$0763017 702 $aGhosh$b Soumya K$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910254843703321 996 $aBankruptcy Prediction through Soft Computing based Deep Learning Technique$92500581 997 $aUNINA