Assembly of classifiers to determine the academic profile of students

dc.creatorSilva, Jesus
dc.creatorRojas Plasencia, Karina Milagros
dc.creatorSenior Naveda, Alexa
dc.creatorBarrios, Rosio
dc.creatorVargas Mercado, Carlos
dc.creatorMedina, Claudia
dc.date2021-01-28T20:00:37Z
dc.date2021-01-28T20:00:37Z
dc.date2020
dc.date.accessioned2023-10-03T19:32:53Z
dc.date.available2023-10-03T19:32:53Z
dc.identifierhttps://hdl.handle.net/11323/7790
dc.identifierhttps://doi.org/10.1016/j.procs.2020.03.102
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9170598
dc.descriptionThe 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.
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherCorporación Universidad de la Costa
dc.relation1 1Zhi-Hua Z. Ensemble methods: Foundations and Algorithms, CRC Press, Taylor & Francis Group (2012)
dc.relation2 Fayyad U., Piatetsky-Shapiro G., Smyth P. From Data Mining to Knowledge Discovery in Databases AI Magazine, 17 (3) (1996), pp. 37-54
dc.relation3 Witten I., Frank E. Data Mining: Practical Machine Learning Tools and Techniques (2nd ed.), Morgan Kaufmann Publishers (2005)
dc.relation4 WEKA 3: Data Mining Software in Java Homepage. https://www.cs.waikato.ac.nz/ ml/weka/ (2016)
dc.relation5 Singh Y., Chanuhan A. Neural Networks in Data Mining Journal of Theorical & Applied Information Technology, 5 (1) (2009), pp. 37-42
dc.relation6 Orallo J., Ramírez M., Ferri C. Introducción a la Minería de Datos, Pearson Education (2008)
dc.relation7 Khasawneh K., Ozsoy M., Ghazaleh N., Ponomarev D. EnsembleHMD: Accurate Hardware Malware Detectors with Specialized Ensemble Classifiers IEEE Transactions on Dependable and Secure Computing, pp., 10 (2018)
dc.relation8 Yan, Y., Yang, H., Wang, H.: Two simple and effective ensemble classifiers for twitter sen- timent analysis. Computing Conference 2017, pp. 1386–1393 (2017)
dc.relation9 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)
dc.relation10 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. 409-436
dc.relation11 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. 481-492
dc.relation12 Aladag, C., & Hocaoglu, G.: A tabu search algorithm to solve a course timetabling problem. Hacettepe journal of mathematics and statistics, pp. 53–64 (2007)
dc.relation13 Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Caltech Concurrent Computation Program (report 826) (1989)
dc.relation14 Frausto-Solís J., Alonso-Pecina F., Mora-Vargas J. An efficient simulated annealing algorithm for feasible solutions of course timetabling, Springer (2008), pp. 675-685
dc.relation15 Joudaki M., Imani M., Mazhari N. Using improved Memetic algorithm and local search to solve University Course Timetabling Problem (UCTTP), Islamic Azad University, Doroud, Iran (2010)
dc.relation16 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. 1201-1206
dc.relation17 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.
dc.relation18 Viloria Amelec, et al. Integration of Data Mining Techniques to PostgreSQL Database Manager System Procedia Computer Science, 155 (2019), pp. 575-580
dc.relation19 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)
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourceProcedia Computer Science
dc.sourcehttps://www.sciencedirect.com/science/article/pii/S1877050920305408#!
dc.subjectAssembly of classifiers
dc.subjectdecision trees
dc.subjectartificial neural network
dc.titleAssembly of classifiers to determine the academic profile of students
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


Este ítem pertenece a la siguiente institución