A machine learning approach for banks classification and forecast

dc.creatorFontalvo Herrera, Tomas
dc.creatorDe La Hoz Dominguez, Enrique
dc.date.accessioned2023-07-14T13:47:54Z
dc.date.accessioned2023-09-06T15:52:27Z
dc.date.available2023-07-14T13:47:54Z
dc.date.available2023-09-06T15:52:27Z
dc.date.created2023-07-14T13:47:54Z
dc.date.issued2019-04
dc.identifierhttps://hdl.handle.net/20.500.12585/12096
dc.identifierUniversidad Tecnológica de Bolívar
dc.identifierRepositorio Universidad Tecnológica de Bolívar
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8683431
dc.description.abstractn this research, a classification model is developed for the banking sector using the machine earning technique GLMNET. In the first place, a clustering process was developed, where 3 clearly differentiated groups were found. Subsequently, a Fuzzy analysis was performed finding the probabilities of transition of the banks to each group found, finally, the GLMNET algorithm was implemented, the automatic classification of the banks according to their financial items, obtaining a result of 95% accuracy. © 2019 International Business Information Management Association (IBIMA).
dc.languageeng
dc.publisherCartagena de Indias
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.sourceProceedings of the 33rd International Business Information Management Association Conference, IBIMA 2019: Education Excellence and Innovation Management through Vision 2020
dc.titleA machine learning approach for banks classification and forecast


Este ítem pertenece a la siguiente institución