Bankruptcy Prediction Through Soft Computing Based Deep Learning Technique

Chaudhuri, Arindam/ Ghosh, Soumya K.

ISBN 10: 9811066825 / ISBN 13: 9789811066825

Published by Springer-Verlag New York Inc, 2017

About this Item

102 pages. 9.00x6.00x0.25 inches. In Stock.

Seller Inventory # x-9811066825

Bibliographic Details

• Title: Bankruptcy Prediction Through Soft Computing Based Deep Learning Technique
• Publisher: Springer-Verlag New York Inc
• Publication Date: 2017
• Language: English
• ISBN 10: 9811066825
• ISBN 13: 9789811066825
• Binding: Paperback
• Condition: Brand New

About this title

Synopsis

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 bookalso 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.

About the Author

Arindam Chaudhuri: Arindam Chaudhuri is currently a Data Scientist at the Samsung R & D Institute Delhi, India. He has worked in industry, research and teaching in the field of machine learning domain for the past 16 years. His current research interests include pattern recognition, machine learning, soft computing, optimization and big data. He received his MTech and PhD in Computer Science from Jadavpur University, Kolkata, India and Netaji Subhas University, Kolkata, India in 2005 and 2011 respectively. He has published 2 research monographs and over 40 articles in international journals and conference proceedings. He has served as a reviewer for several international journals and conferences.

Soumya K Ghosh: Soumya K Ghosh is currently a Professor at the Department of Computer Science Engineering at the Indian Institute of Technology Kharagpur, India. His current research interests include pattern recognition, machine learning, soft computing, cloud applications and sensornetworks. He received his MTech and PhD in Computer Science Engineering from the Indian Institute of Technology Kharagpur, India in 1996 and 2002 respectively. He has over 25 years of experience in industry, research and teaching. He has published 2 research monographs and over 100 articles in international journals and conference proceedings. He has served as a reviewer for several international journals and conferences.