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Bankruptcy prediction through soft computing based deep learning technique [electronic resource]

Bankruptcy prediction through soft computing based deep learning technique [electronic resource]

자료유형
E-Book(소장)
개인저자
Chaudhuri, Arindam. Ghosh, Soumya K.
서명 / 저자사항
Bankruptcy prediction through soft computing based deep learning technique [electronic resource] / Arindam Chaudhuri, Soumya K Ghosh.
발행사항
Singapore :   Springer,   c2017.  
형태사항
1 online resource (xvii, 102 p.) : ill.
ISBN
9789811066825 9789811066832 (eBook)
요약
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.
일반주기
Title from e-Book title page.  
내용주기
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.
서지주기
Includes bibliographical references.
이용가능한 다른형태자료
Issued also as a book.  
일반주제명
Bankruptcy --Forecasting --Data processing. Soft computing. Machine learning.
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020 ▼a 9789811066832 (eBook)
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245 1 0 ▼a Bankruptcy prediction through soft computing based deep learning technique ▼h [electronic resource] / ▼c Arindam Chaudhuri, Soumya K Ghosh.
260 ▼a Singapore : ▼b Springer, ▼c c2017.
300 ▼a 1 online resource (xvii, 102 p.) : ▼b ill.
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references.
505 0 ▼a 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.
520 ▼a 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.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Bankruptcy ▼x Forecasting ▼x Data processing.
650 0 ▼a Soft computing.
650 0 ▼a Machine learning.
700 1 ▼a Ghosh, Soumya K.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=https://doi.org/10.1007/978-981-10-6683-2
945 ▼a KLPA
991 ▼a E-Book(소장)

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/e-Book 컬렉션/ 청구기호 CR 332.75 등록번호 E14014770 도서상태 대출불가(열람가능) 반납예정일 예약 서비스 M

컨텐츠정보

목차

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 .

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Ingham, Geoffrey K. (2020)