Assembly of classifiers to determine the academic profile of students

The 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 apprent...

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Autores: Silva, Jesus, Rojas Plasencia, Karina Milagros, Senior Naveda, Alexa, Barrios, Rosio, Vargas Mercado, Carlos, Medina, Claudia
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2020
País:Colombia
Recursos:Corporación Universidad de la Costa
Repositorio:Repositorio REDICUC
Idioma:inglés
OAI Identifier:oai:repositorio.cuc.edu.co:11323/7790
Acesso em linha:https://hdl.handle.net/11323/7790
https://doi.org/10.1016/j.procs.2020.03.102
https://repositorio.cuc.edu.co/
Access Level:acceso abierto
Palavra-chave:Assembly of classifiers
decision trees
artificial neural network
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dc.title.none.fl_str_mv Assembly of classifiers to determine the academic profile of students
title Assembly of classifiers to determine the academic profile of students
spellingShingle Assembly of classifiers to determine the academic profile of students
Silva, Jesus
Assembly of classifiers
decision trees
artificial neural network
title_short Assembly of classifiers to determine the academic profile of students
title_full Assembly of classifiers to determine the academic profile of students
title_fullStr Assembly of classifiers to determine the academic profile of students
title_full_unstemmed Assembly of classifiers to determine the academic profile of students
title_sort Assembly of classifiers to determine the academic profile of students
dc.creator.none.fl_str_mv Silva, Jesus
Rojas Plasencia, Karina Milagros
Senior Naveda, Alexa
Barrios, Rosio
Vargas Mercado, Carlos
Medina, Claudia
author Silva, Jesus
author_facet Silva, Jesus
Rojas Plasencia, Karina Milagros
Senior Naveda, Alexa
Barrios, Rosio
Vargas Mercado, Carlos
Medina, Claudia
author_role author
author2 Rojas Plasencia, Karina Milagros
Senior Naveda, Alexa
Barrios, Rosio
Vargas Mercado, Carlos
Medina, Claudia
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Assembly of classifiers
decision trees
artificial neural network
topic Assembly of classifiers
decision trees
artificial neural network
description The 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.
publishDate 2020
dc.date.none.fl_str_mv 2020
2021-01-28T20:00:37Z
2021-01-28T20:00:37Z
dc.type.none.fl_str_mv Artículo de revista
http://purl.org/coar/resource_type/c_6501
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dc.identifier.none.fl_str_mv https://hdl.handle.net/11323/7790
https://doi.org/10.1016/j.procs.2020.03.102
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
https://repositorio.cuc.edu.co/
url https://hdl.handle.net/11323/7790
https://doi.org/10.1016/j.procs.2020.03.102
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identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1 1Zhi-Hua Z. Ensemble methods: Foundations and Algorithms, CRC Press, Taylor & Francis Group (2012)
2 Fayyad U., Piatetsky-Shapiro G., Smyth P. From Data Mining to Knowledge Discovery in Databases AI Magazine, 17 (3) (1996), pp. 37-54
3 Witten I., Frank E. Data Mining: Practical Machine Learning Tools and Techniques (2nd ed.), Morgan Kaufmann Publishers (2005)
4 WEKA 3: Data Mining Software in Java Homepage. https://www.cs.waikato.ac.nz/ ml/weka/ (2016)
5 Singh Y., Chanuhan A. Neural Networks in Data Mining Journal of Theorical & Applied Information Technology, 5 (1) (2009), pp. 37-42
6 Orallo J., Ramírez M., Ferri C. Introducción a la Minería de Datos, Pearson Education (2008)
7 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)
8 Yan, Y., Yang, H., Wang, H.: Two simple and effective ensemble classifiers for twitter sen- timent analysis. Computing Conference 2017, pp. 1386–1393 (2017)
9 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)
10 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
11 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
12 Aladag, C., & Hocaoglu, G.: A tabu search algorithm to solve a course timetabling problem. Hacettepe journal of mathematics and statistics, pp. 53–64 (2007)
13 Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Caltech Concurrent Computation Program (report 826) (1989)
14 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
15 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)
16 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
17 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.
18 Viloria Amelec, et al. Integration of Data Mining Techniques to PostgreSQL Database Manager System Procedia Computer Science, 155 (2019), pp. 575-580
19 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)
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spelling Assembly of classifiers to determine the academic profile of studentsSilva, JesusRojas Plasencia, Karina MilagrosSenior Naveda, AlexaBarrios, RosioVargas Mercado, CarlosMedina, ClaudiaAssembly of classifiersdecision treesartificial neural networkThe 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.Silva, JesusRojas Plasencia, Karina Milagros-will be generated-orcid-0000-0001-9324-9478-600Senior Naveda, AlexaBarrios, RosioVargas Mercado, Carlos-will be generated-orcid-0000-0002-5436-0568-600Medina, ClaudiaCorporación Universidad de la Costa2021-01-28T20:00:37Z2021-01-28T20:00:37Z2020Artículo de revistahttp://purl.org/coar/resource_type/c_6501Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/version/c_ab4af688f83e57aaapplication/pdfapplication/pdfhttps://hdl.handle.net/11323/7790https://doi.org/10.1016/j.procs.2020.03.102Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920305408#!reponame:Repositorio REDICUCinstname:Corporación Universidad de la Costainstacron:Corporación Universidad de la Costaeng1 1Zhi-Hua Z. Ensemble methods: Foundations and Algorithms, CRC Press, Taylor & Francis Group (2012)2 Fayyad U., Piatetsky-Shapiro G., Smyth P. From Data Mining to Knowledge Discovery in Databases AI Magazine, 17 (3) (1996), pp. 37-543 Witten I., Frank E. Data Mining: Practical Machine Learning Tools and Techniques (2nd ed.), Morgan Kaufmann Publishers (2005)4 WEKA 3: Data Mining Software in Java Homepage. https://www.cs.waikato.ac.nz/ ml/weka/ (2016)5 Singh Y., Chanuhan A. Neural Networks in Data Mining Journal of Theorical & Applied Information Technology, 5 (1) (2009), pp. 37-426 Orallo J., Ramírez M., Ferri C. Introducción a la Minería de Datos, Pearson Education (2008)7 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)8 Yan, Y., Yang, H., Wang, H.: Two simple and effective ensemble classifiers for twitter sen- timent analysis. Computing Conference 2017, pp. 1386–1393 (2017)9 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)10 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. 409-43611 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. 481-49212 Aladag, C., & Hocaoglu, G.: A tabu search algorithm to solve a course timetabling problem. Hacettepe journal of mathematics and statistics, pp. 53–64 (2007)13 Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Caltech Concurrent Computation Program (report 826) (1989)14 Frausto-Solís J., Alonso-Pecina F., Mora-Vargas J. An efficient simulated annealing algorithm for feasible solutions of course timetabling, Springer (2008), pp. 675-68515 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)16 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. 1201-120617 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.18 Viloria Amelec, et al. Integration of Data Mining Techniques to PostgreSQL Database Manager System Procedia Computer Science, 155 (2019), pp. 575-58019 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)Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf22024-09-17T17:47:10Z
score 15.812429

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