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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]

Material type
E-Book(소장)
Personal Author
Chaudhuri, Arindam. Ghosh, Soumya K.
Title Statement
Bankruptcy prediction through soft computing based deep learning technique [electronic resource] / Arindam Chaudhuri, Soumya K Ghosh.
Publication, Distribution, etc
Singapore :   Springer,   c2017.  
Physical Medium
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.
General Note
Title from e-Book title page.  
Content Notes
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.
Bibliography, Etc. Note
Includes bibliographical references.
이용가능한 다른형태자료
Issued also as a book.  
Subject Added Entry-Topical Term
Bankruptcy --Forecasting --Data processing. Soft computing. Machine learning.
Short cut
URL
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020 ▼a 9789811066825
020 ▼a 9789811066832 (eBook)
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050 4 ▼a HG3761
082 0 4 ▼a 332.75 ▼2 23
084 ▼a 332.75 ▼2 DDCK
090 ▼a 332.75
100 1 ▼a Chaudhuri, Arindam.
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)

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/e-Book Collection/ Call Number CR 332.75 Accession No. E14014770 Availability Loan can not(reference room) Due Date Make a Reservation Service M

Contents information

Table of Contents

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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