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dc.contributor.authorRodan, Ali
dc.contributor.authorCastillo-Valdivieso, Pedro A
dc.contributor.authorFaris, Hossam
dc.contributor.authorAl-Zoubi, Ala’ M
dc.contributor.authorMora, Antonio M
dc.contributor.authorJawazneh, H
dc.date2018
dc.date.accessioned2020-09-09T14:04:38Z
dc.date.available2020-09-09T14:04:38Z
dc.identifier.isbn9782875870476
dc.identifier.urihttps://reunir.unir.net/handle/123456789/10544
dc.descriptionPonencia de la conferencia "26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018; Bruges; Belgium; 25 April 2018 through 27 April 2018"es_ES
dc.description.abstractBankruptcy is a critical financial problem that affects a high number of companies around the world. Thus, in recent years an increasing number of researchers have tried to solve it by applying different machine-learning models as powerful tools for the different economical agents related to the company. In this work, we propose the use of a simple deterministic delay line reservoir (DLR) state space by combining it with three popular classification algorithms (J48, k-NN, and MLP) as an efficient and accurate solution to the bankruptcy prediction problem. The proposed approach is evaluated on a real world dataset collected from Spanish companies. Obtained results show that the proposed models have a higher predictive ability than traditional classification approaches (without DLR reservoir state), resulting in a suitable and efficient alternative approach to solve this complex problem.es_ES
dc.language.isoenges_ES
dc.publisherESANN 2018es_ES
dc.relation.urihttps://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-105.pdfes_ES
dc.rightsopenAccesses_ES
dc.subjectScopus(2)es_ES
dc.titleForecasting business failure in highly imbalanced distribution based on delay line reservoires_ES
dc.typeconferenceObjectes_ES
reunir.tag~ARIes_ES


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