Articoli correlati a Bankruptcy Prediction through Soft Computing based...

Bankruptcy Prediction through Soft Computing based Deep Learning Technique - Brossura

 
9789811066825: Bankruptcy Prediction through Soft Computing based Deep Learning Technique

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull?autore

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 sensor networks. 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.

Dalla quarta di copertina

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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Compra usato

Condizioni: come nuovo
Unread book in perfect condition...
Visualizza questo articolo

EUR 16,98 per la spedizione da U.S.A. a Italia

Destinazione, tempi e costi

EUR 9,70 per la spedizione da Germania a Italia

Destinazione, tempi e costi

Altre edizioni note dello stesso titolo

9789811066849: Bankruptcy Prediction through Soft Computing based Deep Learning Technique

Edizione in evidenza

ISBN 10:  9811066841 ISBN 13:  9789811066849
Casa editrice: Springer, 2017
Brossura

Risultati della ricerca per Bankruptcy Prediction through Soft Computing based...

Immagini fornite dal venditore

Arindam Chaudhuri|Soumya K Ghosh
Editore: Springer Singapore, 2017
ISBN 10: 9811066825 ISBN 13: 9789811066825
Nuovo Brossura
Print on Demand

Da: moluna, Greven, Germania

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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. Codice articolo 160317929

Contatta il venditore

Compra nuovo

EUR 48,74
Convertire valuta
Spese di spedizione: EUR 9,70
Da: Germania a: Italia
Destinazione, tempi e costi

Quantità: Più di 20 disponibili

Aggiungi al carrello

Immagini fornite dal venditore

Arindam Chaudhuri
Editore: SPRINGER NATURE Dez 2017, 2017
ISBN 10: 9811066825 ISBN 13: 9789811066825
Nuovo Taschenbuch
Print on Demand

Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 102 pp. Englisch. Codice articolo 9789811066825

Contatta il venditore

Compra nuovo

EUR 53,49
Convertire valuta
Spese di spedizione: EUR 11,00
Da: Germania a: Italia
Destinazione, tempi e costi

Quantità: 2 disponibili

Aggiungi al carrello

Foto dell'editore

Chaudhuri, Arindam
Editore: Springer, 2017
ISBN 10: 9811066825 ISBN 13: 9789811066825
Nuovo Brossura

Da: Ria Christie Collections, Uxbridge, Regno Unito

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: New. In. Codice articolo ria9789811066825_new

Contatta il venditore

Compra nuovo

EUR 58,83
Convertire valuta
Spese di spedizione: EUR 10,37
Da: Regno Unito a: Italia
Destinazione, tempi e costi

Quantità: Più di 20 disponibili

Aggiungi al carrello

Immagini fornite dal venditore

Chaudhuri, Arindam; Ghosh, Soumya K.
Editore: Springer, 2017
ISBN 10: 9811066825 ISBN 13: 9789811066825
Nuovo Brossura

Da: GreatBookPrices, Columbia, MD, U.S.A.

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: New. Codice articolo 30001803-n

Contatta il venditore

Compra nuovo

EUR 52,93
Convertire valuta
Spese di spedizione: EUR 16,98
Da: U.S.A. a: Italia
Destinazione, tempi e costi

Quantità: Più di 20 disponibili

Aggiungi al carrello

Immagini fornite dal venditore

Arindam Chaudhuri
Editore: Springer Nature Singapore, 2017
ISBN 10: 9811066825 ISBN 13: 9789811066825
Nuovo Taschenbuch

Da: AHA-BUCH GmbH, Einbeck, Germania

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Codice articolo 9789811066825

Contatta il venditore

Compra nuovo

EUR 56,98
Convertire valuta
Spese di spedizione: EUR 14,99
Da: Germania a: Italia
Destinazione, tempi e costi

Quantità: 2 disponibili

Aggiungi al carrello

Foto dell'editore

Chaudhuri, Arindam
Editore: Springer, 2017
ISBN 10: 9811066825 ISBN 13: 9789811066825
Nuovo Brossura

Da: California Books, Miami, FL, U.S.A.

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: New. Codice articolo I-9789811066825

Contatta il venditore

Compra nuovo

EUR 64,74
Convertire valuta
Spese di spedizione: EUR 7,64
Da: U.S.A. a: Italia
Destinazione, tempi e costi

Quantità: Più di 20 disponibili

Aggiungi al carrello

Immagini fornite dal venditore

Chaudhuri, Arindam; Ghosh, Soumya K.
Editore: Springer, 2017
ISBN 10: 9811066825 ISBN 13: 9789811066825
Nuovo Brossura

Da: GreatBookPricesUK, Woodford Green, Regno Unito

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: New. Codice articolo 30001803-n

Contatta il venditore

Compra nuovo

EUR 58,82
Convertire valuta
Spese di spedizione: EUR 17,30
Da: Regno Unito a: Italia
Destinazione, tempi e costi

Quantità: Più di 20 disponibili

Aggiungi al carrello

Immagini fornite dal venditore

Chaudhuri, Arindam; Ghosh, Soumya K.
Editore: Springer, 2017
ISBN 10: 9811066825 ISBN 13: 9789811066825
Antico o usato Brossura

Da: GreatBookPrices, Columbia, MD, U.S.A.

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: As New. Unread book in perfect condition. Codice articolo 30001803

Contatta il venditore

Compra usato

EUR 60,27
Convertire valuta
Spese di spedizione: EUR 16,98
Da: U.S.A. a: Italia
Destinazione, tempi e costi

Quantità: Più di 20 disponibili

Aggiungi al carrello

Foto dell'editore

Chaudhuri, Arindam
Editore: Springer, 2017
ISBN 10: 9811066825 ISBN 13: 9789811066825
Nuovo Brossura

Da: Books Puddle, New York, NY, U.S.A.

Valutazione del venditore 4 su 5 stelle 4 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: New. pp. 102. Codice articolo 26375555831

Contatta il venditore

Compra nuovo

EUR 74,96
Convertire valuta
Spese di spedizione: EUR 7,64
Da: U.S.A. a: Italia
Destinazione, tempi e costi

Quantità: 4 disponibili

Aggiungi al carrello

Immagini fornite dal venditore

Chaudhuri, Arindam; Ghosh, Soumya K.
Editore: Springer, 2017
ISBN 10: 9811066825 ISBN 13: 9789811066825
Antico o usato Brossura

Da: GreatBookPricesUK, Woodford Green, Regno Unito

Valutazione del venditore 5 su 5 stelle 5 stelle, Maggiori informazioni sulle valutazioni dei venditori

Condizione: As New. Unread book in perfect condition. Codice articolo 30001803

Contatta il venditore

Compra usato

EUR 65,70
Convertire valuta
Spese di spedizione: EUR 17,30
Da: Regno Unito a: Italia
Destinazione, tempi e costi

Quantità: Più di 20 disponibili

Aggiungi al carrello

Vedi altre 5 copie di questo libro

Vedi tutti i risultati per questo libro