Publication
High-performance bankruptcy prediction model using Graphics Processing Units
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | |
| datacite.subject.sdg | 03:Saúde de Qualidade | |
| datacite.subject.sdg | 07:Energias Renováveis e Acessíveis | |
| datacite.subject.sdg | 11:Cidades e Comunidades Sustentáveis | |
| dc.contributor.author | Ribeiro, Bernardete | |
| dc.contributor.author | Lopes, Noel | |
| dc.contributor.author | Silva, Catarina | |
| dc.date.accessioned | 2025-11-20T12:16:16Z | |
| dc.date.available | 2025-11-20T12:16:16Z | |
| dc.date.issued | 2010-07 | |
| dc.description | EISBN - 978-1-4244-6918-5 | |
| dc.description | Conference name - 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010; Conference date - 18 July 2010 - 23 July 2010; Conference code - 85188 | |
| dc.description | Fonte: https://www.researchgate.net/publication/224181340_IJCNN_High-Performance_Bankruptcy_Prediction_Model_using_Graphics_Processing_Units | |
| dc.description.abstract | In recent years the the potential and programmability of Graphics Processing Units (GPU) has raised a note-worthy interest in the research community for applications that demand high-computational power. In particular, in financial applications containing thousands of high-dimensional samples, machine learning techniques such as neural networks are often used. One of their main limitations is that the learning phase can be extremely consuming due to the long training times required which constitute a hard bottleneck for their use in practice. Thus their implementation in graphics hardware is highly desirable as a way to speed up the training process. In this paper we present a bankruptcy prediction model based on the parallel implementation of the Multiple BackPropagation (MBP) algorithm which is tested on a real data set of French companies (healthy and bankrupt). Results by running the MBP algorithm in a sequential processing CPU version and in a parallel GPU implementation show reduced computational costs with respect to the latter while yielding very competitive performance. | eng |
| dc.description.sponsorship | Financial support from “Fundação da Ciência e Tecnologia” under the project PTDC/GES/70168/2006 is gratefully acknowledged. | |
| dc.identifier.citation | B. Ribeiro, N. Lopes and C. Silva, "High-performance bankruptcy prediction model using Graphics Processing Units," The 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, 2010, pp. 1-7, doi: https://doi.org/10.1109/IJCNN.2010.5596711. | |
| dc.identifier.doi | 10.1109/ijcnn.2010.5596711 | |
| dc.identifier.eissn | 2161-4407 | |
| dc.identifier.isbn | 978-1-4244-6916-1 | |
| dc.identifier.isbn | 978-1-4244-6918-5 | |
| dc.identifier.issn | 2161-4393 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/14688 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | IEEE Canada | |
| dc.relation.hasversion | https://ieeexplore.ieee.org/document/5596711 | |
| dc.relation.ispartof | The 2010 International Joint Conference on Neural Networks (IJCNN) | |
| dc.rights.uri | N/A | |
| dc.subject | Graphics processing unit | |
| dc.subject | Neurons | |
| dc.subject | Predictive models | |
| dc.subject | Computational modeling | |
| dc.subject | Artificial neural networks | |
| dc.subject | Training | |
| dc.subject | Companies | |
| dc.title | High-performance bankruptcy prediction model using Graphics Processing Units | eng |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.citation.conferenceDate | 2010-07 | |
| oaire.citation.conferencePlace | Barcelona, Spain | |
| oaire.citation.endPage | 2216 | |
| oaire.citation.startPage | 2210 | |
| oaire.citation.title | Proceedings of the International Joint Conference on Neural Networks | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Silva | |
| person.givenName | Catarina | |
| person.identifier.ciencia-id | 1B19-3DDC-BE75 | |
| person.identifier.orcid | 0000-0002-5656-0061 | |
| relation.isAuthorOfPublication | ee28e079-5ca7-4842-9094-372c40f75c38 | |
| relation.isAuthorOfPublication.latestForDiscovery | ee28e079-5ca7-4842-9094-372c40f75c38 |
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