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Assembly of classifiers to determine the academic profile of students

dc.contributor.authorSilva, Jesusspa
dc.contributor.authorRojas Plasencia, Karina Milagrosspa
dc.contributor.authorSenior Naveda, Alexaspa
dc.contributor.authorBarrios, Rosiospa
dc.contributor.authorVargas Mercado, Carlosspa
dc.contributor.authorMedina, Claudiaspa
dc.date.accessioned2021-01-28T20:00:37Z
dc.date.available2021-01-28T20:00:37Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/11323/7790spa
dc.description.abstractThe assembly methods, or combination of models, arise with the purpose of improving the accuracy of predictions. An assembly contains a number of apprentices (base models) which, when of the same type are called homogeneous and if of different, heterogeneous. The characteristic is that these apprentices do not perform well. The assembly is generated using another algorithm that combines the apprentices, examples of which are the majority vote, the decision table and the neural networks [1]. This article proposes the use of an assembly of classifiers to determine the academic profile of the student, based on the student’s overall average and data related to educational factors.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.sourceProcedia Computer Sciencespa
dc.subjectAssembly of classifiersspa
dc.subjectdecision treesspa
dc.subjectartificial neural networkspa
dc.titleAssembly of classifiers to determine the academic profile of studentsspa
dc.typeArtículo de revistaspa
dc.source.urlhttps://www.sciencedirect.com/science/article/pii/S1877050920305408#!spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1016/j.procs.2020.03.102spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.relation.references1 1Zhi-Hua Z. Ensemble methods: Foundations and Algorithms, CRC Press, Taylor & Francis Group (2012)spa
dc.relation.references2 Fayyad U., Piatetsky-Shapiro G., Smyth P. From Data Mining to Knowledge Discovery in Databases AI Magazine, 17 (3) (1996), pp. 37-54spa
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dc.relation.references9 Vogado, L., Veras, R., Andrade, A., Araujo, F., Silva, R., Aires, K.: Diagnosing Leukemia in Blood Smear Images Using an Ensemble of Classifiers and Pre-Trained Convolutional Neural Networks. 30th (SIBGRAPI) Conference on Graphics, Patterns and Images, pp. 367– 373, Niteroi (2017)spa
dc.relation.references10 Hestenes M., Stiefel E. Methods of Conjugate Gradients for Solving Linear Systems Journal of Research of the National Bureau of Standards, 49 (6) (1952), pp. 409-436spa
dc.relation.references11 C. Sotelo-Figueroa, M.A. Castillo, Melin P.O., Pedrycz W., Kacprzyk J. Generic Memetic Algorithm for Course Timetabling ITC2007 Recent Advances on Hybrid Approaches for Designing Intelligent Systems, Springer (2014), pp. 481-492spa
dc.relation.references12 Aladag, C., & Hocaoglu, G.: A tabu search algorithm to solve a course timetabling problem. Hacettepe journal of mathematics and statistics, pp. 53–64 (2007)spa
dc.relation.references13 Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Caltech Concurrent Computation Program (report 826) (1989)spa
dc.relation.references14 Frausto-Solís J., Alonso-Pecina F., Mora-Vargas J. An efficient simulated annealing algorithm for feasible solutions of course timetabling, Springer (2008), pp. 675-685spa
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dc.relation.references16 Viloria Amelec, Lezama Omar Bonerge Pineda Improvements for Determining the Number of Clusters in k-Means for Innovation Databases in SMEs Procedia Computer Science, 151 (2019), pp. 1201-1206spa
dc.relation.references17 Kamatkar, S.J., Kamble, A., Viloria, A., Hernández-Fernández, L., & Cali, E.G. (2018, June). Database performance tuning and query optimization. In International Conference on Data Mining and Big Data (pp. 3-11). Springer, Cham.spa
dc.relation.references18 Viloria Amelec, et al. Integration of Data Mining Techniques to PostgreSQL Database Manager System Procedia Computer Science, 155 (2019), pp. 575-580spa
dc.relation.references19 Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching - Learning Process Through Knowledge Data Discovery (Big Data). Tan Y., Shi Y., Tang Q. (Eds.), Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943, Springer, Cham (2018)spa
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dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa


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