Bayesian classifier applied to higher education dropout
The research proposes a new simple Bayesian classifier (SBND) with Markov from the class variable to a network structure. Experimental tests are carried out by working a dropout analysis on students enrolled in the Faculty of Engineering Sciences of Mumbai University, in India in the period 2017-201...
- Autores:
-
amelec, viloria
Pineda Lezama, Omar Bonerge
Varela Izquierdo, Noel
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/5977
- Acceso en línea:
- https://hdl.handle.net/11323/5977
https://repositorio.cuc.edu.co/
- Palabra clave:
- Bayesian networks
Bayesian classifier
Educational analysis
Redes bayesianas
Clasificacion bayesiana
Analisis educacional
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Bayesian classifier applied to higher education dropout |
| dc.title.translated.spa.fl_str_mv |
Clasificador bayesiano aplicado al abandono de la educación superior |
| title |
Bayesian classifier applied to higher education dropout |
| spellingShingle |
Bayesian classifier applied to higher education dropout Bayesian networks Bayesian classifier Educational analysis Redes bayesianas Clasificacion bayesiana Analisis educacional |
| title_short |
Bayesian classifier applied to higher education dropout |
| title_full |
Bayesian classifier applied to higher education dropout |
| title_fullStr |
Bayesian classifier applied to higher education dropout |
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Bayesian classifier applied to higher education dropout |
| title_sort |
Bayesian classifier applied to higher education dropout |
| dc.creator.fl_str_mv |
amelec, viloria Pineda Lezama, Omar Bonerge Varela Izquierdo, Noel |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria Pineda Lezama, Omar Bonerge Varela Izquierdo, Noel |
| dc.subject.spa.fl_str_mv |
Bayesian networks Bayesian classifier Educational analysis Redes bayesianas Clasificacion bayesiana Analisis educacional |
| topic |
Bayesian networks Bayesian classifier Educational analysis Redes bayesianas Clasificacion bayesiana Analisis educacional |
| description |
The research proposes a new simple Bayesian classifier (SBND) with Markov from the class variable to a network structure. Experimental tests are carried out by working a dropout analysis on students enrolled in the Faculty of Engineering Sciences of Mumbai University, in India in the period 2017-2018 on the basis of socioeconomic data. The Weka tool is then used to perform the classification and the proposed model is statistically compared with other Bayesian classifiers. |
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2019 |
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2019 |
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2020-02-03T19:28:28Z |
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2020-02-03T19:28:28Z |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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00002010 |
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https://hdl.handle.net/11323/5977 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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00002010 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
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Torres-Samuel, M., Vásquez, C., Viloria, A., Lis-Gutiérrez, J.P., Borrero, T.C., Varela, N.: Web Visibility Profiles of Top100 Latin American Universities. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, Springer, Cham, vol 10943, 1-12 (2018). Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50 (1), 159-75 (2003). Duan, L., Xu, L., Liu, Y., Lee, J.: Cluster-based outlier detection. Annals of Operations Research 168 (1), 151–168 (2009). Haykin, S.: Neural Networks a Comprehensive Foundation. Second Edition. Macmillan College Publishing, Inc. USA. ISBN 9780023527616 (1999). Haykin, S.: Neural Networks and Learning Machines. New Jersey, Prentice Hall International (2009). Oviedo, B. a. (2015). Análisis de datos educativos utilizando redes bayesianas, Latin American and Caribbean Conference for Engineering and Technology LACCEI 2015. Abhay, K. A., Badal, N. A.: Novel Approach for Intelligent Distribution of Data Warehouses. Published in Egyptian Informatics JournalElsevier, Egypt 17 (1), 147-159, (October, 2015). Vasquez, C., Torres, M., Viloria, A.: Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J. Eng. Appl. Sci. 12(11), 2963–2965 (2017). Vásquez, C., Torres-Samuel, M., Viloria, A., Lis-Gutiérrez, J.P., Crissien Borrero, T., Varela, N., Cabrera, D.: Cluster of the Latin American Universities Top100 According to Webometrics 2017. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, Springer, Cham, vol 10943, 1-12 (2018). Sevim, C., Oztekin, A., Bali, O., Gumus, S., Guresen, E.: Developing an early warning system to predict currency crises. European Journal of Operational Research 237(1), 1095-104 (2014). Viloria, A., Lis-Gutiérrez, J.P., 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). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. Soca, E.B., El trabajo independiente en el proceso de enseñanza-aprendizaje, ISSN: 1684-1859, Revista Cubana de Informática Médica, 7(2), 122-131 (2015) Vanyolos, E., I. Furka, I. Miko y otros tres autores. How does practice improve the skills of medical students during consecutive training courses? doi; https://dx.doi.org/10.1590/s0102-865020170060000010. Rev. Acta Cirurgica Brasileira, 32(6), 491-502 (2017) Isasi, P., Galván, I.: Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson. ISBN 8420540250 (2004). Haykin, S.: Neural Networks and Learning Machines. New Jersey, Prentice Hall International (2009). |
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amelec, viloriaPineda Lezama, Omar BonergeVarela Izquierdo, Noel2020-02-03T19:28:28Z2020-02-03T19:28:28Z201900002010https://hdl.handle.net/11323/5977Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The research proposes a new simple Bayesian classifier (SBND) with Markov from the class variable to a network structure. Experimental tests are carried out by working a dropout analysis on students enrolled in the Faculty of Engineering Sciences of Mumbai University, in India in the period 2017-2018 on the basis of socioeconomic data. The Weka tool is then used to perform the classification and the proposed model is statistically compared with other Bayesian classifiers.La investigación propone un nuevo clasificador bayesiano simple (SBND) con Markov de la variable de clase a una estructura de red. Las pruebas experimentales se llevan a cabo mediante un análisis de abandono en los estudiantes matriculados en la Facultad de Ciencias de la Ingeniería de la Universidad de Mumbai, en la India, en el período 2017-2018 sobre la base de datos socioeconómicos. La herramienta Weka se utiliza para realizar la clasificación y el modelo propuesto se compara estadísticamente con otros clasificadores bayesianos.Amelec, Viloria-will be generated-orcid-0000-0003-2673-6350-600Pineda Lezama, Omar BonergeVarela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600engProcedia Computer ScienceCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Bayesian networksBayesian classifierEducational analysisRedes bayesianasClasificacion bayesianaAnalisis educacionalBayesian classifier applied to higher education dropoutClasificador bayesiano aplicado al abandono de la educación superiorArtículo 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/acceptedVersionTorres-Samuel, M., Vásquez, C., Viloria, A., Lis-Gutiérrez, J.P., Borrero, T.C., Varela, N.: Web Visibility Profiles of Top100 Latin American Universities. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, Springer, Cham, vol 10943, 1-12 (2018).Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50 (1), 159-75 (2003).Duan, L., Xu, L., Liu, Y., Lee, J.: Cluster-based outlier detection. Annals of Operations Research 168 (1), 151–168 (2009).Haykin, S.: Neural Networks a Comprehensive Foundation. Second Edition. Macmillan College Publishing, Inc. USA. ISBN 9780023527616 (1999).Haykin, S.: Neural Networks and Learning Machines. New Jersey, Prentice Hall International (2009).Oviedo, B. a. (2015). Análisis de datos educativos utilizando redes bayesianas, Latin American and Caribbean Conference for Engineering and Technology LACCEI 2015.Abhay, K. A., Badal, N. A.: Novel Approach for Intelligent Distribution of Data Warehouses. Published in Egyptian Informatics JournalElsevier, Egypt 17 (1), 147-159, (October, 2015).Vasquez, C., Torres, M., Viloria, A.: Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J. Eng. Appl. Sci. 12(11), 2963–2965 (2017).Vásquez, C., Torres-Samuel, M., Viloria, A., Lis-Gutiérrez, J.P., Crissien Borrero, T., Varela, N., Cabrera, D.: Cluster of the Latin American Universities Top100 According to Webometrics 2017. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, Springer, Cham, vol 10943, 1-12 (2018).Sevim, C., Oztekin, A., Bali, O., Gumus, S., Guresen, E.: Developing an early warning system to predict currency crises. European Journal of Operational Research 237(1), 1095-104 (2014).Viloria, A., Lis-Gutiérrez, J.P., 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). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data.Soca, E.B., El trabajo independiente en el proceso de enseñanza-aprendizaje, ISSN: 1684-1859, Revista Cubana de Informática Médica, 7(2), 122-131 (2015)Vanyolos, E., I. Furka, I. Miko y otros tres autores. How does practice improve the skills of medical students during consecutive training courses? doi; https://dx.doi.org/10.1590/s0102-865020170060000010. Rev. Acta Cirurgica Brasileira, 32(6), 491-502 (2017)Isasi, P., Galván, I.: Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson. ISBN 8420540250 (2004).Haykin, S.: Neural Networks and Learning Machines. New Jersey, Prentice Hall International (2009).PublicationCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/1b398a81-d757-4de5-9915-c59a8f4b70eb/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/25f4a3dc-5313-4810-b43d-5d297c51c61e/download8a4605be74aa9ea9d79846c1fba20a33MD53ORIGINALBayesian Classifier Applied to Higher Education Dropout (1).pdfBayesian Classifier Applied to Higher Education Dropout (1).pdfapplication/pdf376918https://repositorio.cuc.edu.co/bitstreams/532e0e30-980e-4a44-bfb9-13019d037a84/download4eeb113922a03bb47ff3e80c8eeec20dMD51THUMBNAILBayesian Classifier Applied to Higher Education Dropout (1).pdf.jpgBayesian Classifier Applied to Higher Education Dropout (1).pdf.jpgimage/jpeg44585https://repositorio.cuc.edu.co/bitstreams/fed30181-b12d-4d9d-b45e-62149be1cbb3/download46e6bd30e20f352cd82cc37c6cf73ac7MD55TEXTBayesian Classifier Applied to Higher Education Dropout (1).pdf.txtBayesian Classifier Applied to Higher Education Dropout (1).pdf.txttext/plain15722https://repositorio.cuc.edu.co/bitstreams/127c5347-cce1-4ff7-88b0-4de0eb07a3bf/download9a593d01032dd8337d530c3461a7ece1MD5611323/5977oai:repositorio.cuc.edu.co:11323/59772024-09-17 10:14:21.828http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.coTk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo= |