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...
- Autores:
-
Silva, Jesus
Rojas Plasencia, Karina Milagros
Senior Naveda, Alexa
Barrios, Rosio
Vargas Mercado, Carlos
Medina, Claudia
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7790
- Acceso en línea:
- https://hdl.handle.net/11323/7790
https://doi.org/10.1016/j.procs.2020.03.102
https://repositorio.cuc.edu.co/
- Palabra clave:
- Assembly of classifiers
decision trees
artificial neural network
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Assembly of classifiers to determine the academic profile of students |
| title |
Assembly of classifiers to determine the academic profile of students |
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Assembly of classifiers to determine the academic profile of students Assembly of classifiers decision trees artificial neural network |
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Assembly of classifiers to determine the academic profile of students |
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Assembly of classifiers to determine the academic profile of students |
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Assembly of classifiers to determine the academic profile of students |
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Assembly of classifiers to determine the academic profile of students |
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Assembly of classifiers to determine the academic profile of students |
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Silva, Jesus Rojas Plasencia, Karina Milagros Senior Naveda, Alexa Barrios, Rosio Vargas Mercado, Carlos Medina, Claudia |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesus Rojas Plasencia, Karina Milagros Senior Naveda, Alexa Barrios, Rosio Vargas Mercado, Carlos Medina, Claudia |
| dc.subject.spa.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. |
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eng |
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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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Silva, JesusRojas Plasencia, Karina MilagrosSenior Naveda, AlexaBarrios, RosioVargas Mercado, CarlosMedina, Claudia2021-01-28T20:00:37Z2021-01-28T20:00:37Z2020https://hdl.handle.net/11323/7790https://doi.org/10.1016/j.procs.2020.03.102Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.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, Claudiaapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920305408#!Assembly of classifiersdecision treesartificial neural networkAssembly of classifiers to determine the academic profile of studentsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1 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)PublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/f7568f06-10ef-452b-b53c-a92eca3a91f6/downloade30e9215131d99561d40d6b0abbe9badMD53ORIGINALAssembly of classifiers to determine the academic profile of students.pdfAssembly of classifiers to determine the academic profile of students.pdfapplication/pdf100551https://repositorio.cuc.edu.co/bitstreams/611f8b33-d082-4edb-ae18-89fbd07824d3/download5bf22d6567caacd85f57ffb656fa27a4MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/a1c51890-b739-448b-931c-fe6391e87184/download4460e5956bc1d1639be9ae6146a50347MD52THUMBNAILAssembly of classifiers to determine the academic profile of students.pdf.jpgAssembly of classifiers to determine the academic profile of 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