Integration of data mining classification techniques and ensemble learning for predicting the export potential of a company
In this research, data mining techniques are integrated with Ensemble Learning for predicting the export potential of a company. The analysis covers the stages of measurement, evaluation and classification of companies, based on a proposal of 16 key factors of the export potential. The techniques st...
- Autores:
-
Silva, Jesus
Romero Borré, Jenny
Piñeres Castillo, Aurora Patricia
Castro, Ligia
Varela, 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:11323/4833
- Acceso en línea:
- https://hdl.handle.net/11323/4833
https://repositorio.cuc.edu.co/
- Palabra clave:
- K-Means clustering
classification models
export potential
competitiveness
data mining
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Integration of data mining classification techniques and ensemble learning for predicting the export potential of a company |
| title |
Integration of data mining classification techniques and ensemble learning for predicting the export potential of a company |
| spellingShingle |
Integration of data mining classification techniques and ensemble learning for predicting the export potential of a company K-Means clustering classification models export potential competitiveness data mining |
| title_short |
Integration of data mining classification techniques and ensemble learning for predicting the export potential of a company |
| title_full |
Integration of data mining classification techniques and ensemble learning for predicting the export potential of a company |
| title_fullStr |
Integration of data mining classification techniques and ensemble learning for predicting the export potential of a company |
| title_full_unstemmed |
Integration of data mining classification techniques and ensemble learning for predicting the export potential of a company |
| title_sort |
Integration of data mining classification techniques and ensemble learning for predicting the export potential of a company |
| dc.creator.fl_str_mv |
Silva, Jesus Romero Borré, Jenny Piñeres Castillo, Aurora Patricia Castro, Ligia Varela, Noel |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesus Romero Borré, Jenny Piñeres Castillo, Aurora Patricia Castro, Ligia Varela, Noel |
| dc.subject.spa.fl_str_mv |
K-Means clustering classification models export potential competitiveness data mining |
| topic |
K-Means clustering classification models export potential competitiveness data mining |
| description |
In this research, data mining techniques are integrated with Ensemble Learning for predicting the export potential of a company. The analysis covers the stages of measurement, evaluation and classification of companies, based on a proposal of 16 key factors of the export potential. The techniques standing out are: Synthetic Minority Oversampling Technique (Smote), K-Means Clustering, Generalized Regression Neural Network (GRNN), Feed Forward Back Propagation Neural Network (FFBPN), Support Vector Machine (SVM), Decision Tree (DT) and Naive Bayes. The neural network classifiers like GRNN and FFBPN are used for classification in MATLAB in the numeric form of data with a training and testing data ratio of 70% and 30% respectively. The accuracy of other classifiers such as SVM, DT and Naive Bayes is calculated on the nominal form of data with 80% data split. Artificial neural networks showed 85.7% of ability to discriminate and classify companies according to their competitive profile. |
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2019 |
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2019-06-10T13:19:20Z |
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2019-06-10T13:19:20Z |
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2019 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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0000-2010 |
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https://hdl.handle.net/11323/4833 |
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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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eng |
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eng |
| dc.relation.references.spa.fl_str_mv |
[1] Escandón, D., y Hurtado, A., Los determinantes de la orientación exportadora y los resultados en las pymes exportadoras en Colombia, Estudios Gerenciales, 30(133), 430–440 (2014). [2] Cabarcas, J., y Paternina, C., Aplicación del análisis discriminante para identificar diferencias en el perfil productivo de las empresas exportadoras y no exportadoras del Departamento del Atlántico de Colombia, Revista Ingeniare, 6(10), 33–48 (2011) [3] Smith, D., A Neural Network Classification of Export Success in Japanese Service Firms, Services Marketing Quarterly, 26(4), 95–108 (2005). [4] Correia, A., Barandas, H., y PIres, P., Applying Artificial Neural Networks to Evaluate Export Performance : A Relational Approach, Review of Onternational Comparative Management, 10(4), 713–734 (2009) [5] Obschatko, E., y Blaio, M., El perfil exportador del sector agroalimentario argentino. Las profucciones de alto valor. Estudio 1. EG.33.7. Ministerio de Economía de Argentina (2003) [6] Paredes, D., Elaboración del plan de negocios de exportación. Programa de Plan de Negocio, Exportador- PLANEX. Disponible en: https://goo.gl/oTnARL (2016) [7] Lis-Gutiérrez JP., Gaitán-Angulo M., Balaguera MI., Viloria A., Santander-Abril JE. (2018) Use of the Industrial Property System for New Creations in Colombia: A Departmental Analysis (2000–2016). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [8] Caridad, J. M., & Ceular, N. (2001). “Un análisis del mercado de la vivienda a través de redes neuronales artificiales”. Estudios de economía aplicada, (18), pp. 67-81. [9] Uberbacher, E. C., & Mural, R. J. (1991). “Locating protein-coding regions in human DNA sequences by a multiple sensor-neural network approach”. Proceedings of the National Academy of Sciences, 88(24), pp.11261-11265. [10] Olmedo, E., Velasco, F., & Valderas, J. M. (2007). “Caracterización no lineal y predicción no paramétrica en el IBEX35”. Estudios de Economía Aplicada, 25(3). [11] De La Hoz, E., González, Á., y Santana, A., Metodología de Medición del Potencial Exportador de las Organizaciones Empresariales, Información Tecnológica, 27(6), 11–18 (2016) [12] De La Hoz, E., López P. Aplicación de Técnicas de Análisis de Conglomerados y Redes Neuronales Artificiales en la Evaluación del Potencial Exportador de una Empresa. Información Tecnológica. Vol. 28(4), 67-74 (2017). [13] Qazi, N. Effectof Feature Selection, Synthetic Minority Over-sampling (SMOTE) And Under- sampling on Class imbalance Classification. https://doi.org/10.1109/UKSim.116 (2012) [14] Sharmila, S., & Kumar, M. An optimized farthest first clustering algorithm.Nirma University International Conference on Engineering, NUiCONE 2013, 1–5. https://doi.org/10.1109/NUiCONE.2013.6780070 (2013) [15] Kumar, G., & Malik, H. Generalized Regression Neural Network Based Wind Speed Prediction Model for Western Region of India. Procedia Computer Science, 93(September), 26–32. https://doi.org/10.1016/j.procs.07.177 (2016) [16] Sun, G., Hoff, S., Zelle, B., & Nelson, M.Development and Comparison of Backpropagation and Generalized Regression Neural Network Models to Predict Diurnal and Seasonal Gas and PM 10 Concentrations and Emissions from Swine Buildings, 0300(08) (2008). [17] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company. Indian Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371 [18] Amelec, V., & Alexander, P. (2015). Improvements in the Automatic Distribution Process of Finished Product for Pet Food Category in Multinational Company. Advanced Science Letters, 21(5), 1419-1421. [19] Viloria, A., & Robayo, P. V. (2016). Inventory reduction in the supply chain of finished products for multinational companies. Indian Journal of Science and Technology, 8(1). |
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Silva, JesusRomero Borré, JennyPiñeres Castillo, Aurora PatriciaCastro, LigiaVarela, Noel2019-06-10T13:19:20Z2019-06-10T13:19:20Z20190000-2010https://hdl.handle.net/11323/4833Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/In this research, data mining techniques are integrated with Ensemble Learning for predicting the export potential of a company. The analysis covers the stages of measurement, evaluation and classification of companies, based on a proposal of 16 key factors of the export potential. The techniques standing out are: Synthetic Minority Oversampling Technique (Smote), K-Means Clustering, Generalized Regression Neural Network (GRNN), Feed Forward Back Propagation Neural Network (FFBPN), Support Vector Machine (SVM), Decision Tree (DT) and Naive Bayes. The neural network classifiers like GRNN and FFBPN are used for classification in MATLAB in the numeric form of data with a training and testing data ratio of 70% and 30% respectively. The accuracy of other classifiers such as SVM, DT and Naive Bayes is calculated on the nominal form of data with 80% data split. Artificial neural networks showed 85.7% of ability to discriminate and classify companies according to their competitive profile.Silva, Jesus-60750872-819f-4163-bbb8-c33aee0e2cf1-0Romero Borré, Jenny-ed4876b7-e00b-4362-88e3-7fd43bd7e710-0Piñeres Castillo, Aurora Patricia-0000-0003-2445-8297-600Castro, Ligia-d779f322-c772-4069-835f-e62e7b4f7aa7-0Varela, Noel-e2c4502e-24e6-484a-9820-77a41202aeca-0engProcedia Computer ScienceAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2K-Means clusteringclassification modelsexport potentialcompetitivenessdata miningIntegration of data mining classification techniques and ensemble learning for predicting the export potential of a companyArtículo 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/acceptedVersion[1] Escandón, D., y Hurtado, A., Los determinantes de la orientación exportadora y los resultados en las pymes exportadoras en Colombia, Estudios Gerenciales, 30(133), 430–440 (2014). [2] Cabarcas, J., y Paternina, C., Aplicación del análisis discriminante para identificar diferencias en el perfil productivo de las empresas exportadoras y no exportadoras del Departamento del Atlántico de Colombia, Revista Ingeniare, 6(10), 33–48 (2011) [3] Smith, D., A Neural Network Classification of Export Success in Japanese Service Firms, Services Marketing Quarterly, 26(4), 95–108 (2005). [4] Correia, A., Barandas, H., y PIres, P., Applying Artificial Neural Networks to Evaluate Export Performance : A Relational Approach, Review of Onternational Comparative Management, 10(4), 713–734 (2009) [5] Obschatko, E., y Blaio, M., El perfil exportador del sector agroalimentario argentino. Las profucciones de alto valor. Estudio 1. EG.33.7. Ministerio de Economía de Argentina (2003) [6] Paredes, D., Elaboración del plan de negocios de exportación. Programa de Plan de Negocio, Exportador- PLANEX. Disponible en: https://goo.gl/oTnARL (2016) [7] Lis-Gutiérrez JP., Gaitán-Angulo M., Balaguera MI., Viloria A., Santander-Abril JE. (2018) Use of the Industrial Property System for New Creations in Colombia: A Departmental Analysis (2000–2016). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham [8] Caridad, J. M., & Ceular, N. (2001). “Un análisis del mercado de la vivienda a través de redes neuronales artificiales”. Estudios de economía aplicada, (18), pp. 67-81. [9] Uberbacher, E. C., & Mural, R. J. (1991). “Locating protein-coding regions in human DNA sequences by a multiple sensor-neural network approach”. Proceedings of the National Academy of Sciences, 88(24), pp.11261-11265. [10] Olmedo, E., Velasco, F., & Valderas, J. M. (2007). “Caracterización no lineal y predicción no paramétrica en el IBEX35”. Estudios de Economía Aplicada, 25(3). [11] De La Hoz, E., González, Á., y Santana, A., Metodología de Medición del Potencial Exportador de las Organizaciones Empresariales, Información Tecnológica, 27(6), 11–18 (2016) [12] De La Hoz, E., López P. Aplicación de Técnicas de Análisis de Conglomerados y Redes Neuronales Artificiales en la Evaluación del Potencial Exportador de una Empresa. Información Tecnológica. Vol. 28(4), 67-74 (2017). [13] Qazi, N. Effectof Feature Selection, Synthetic Minority Over-sampling (SMOTE) And Under- sampling on Class imbalance Classification. https://doi.org/10.1109/UKSim.116 (2012) [14] Sharmila, S., & Kumar, M. An optimized farthest first clustering algorithm.Nirma University International Conference on Engineering, NUiCONE 2013, 1–5. https://doi.org/10.1109/NUiCONE.2013.6780070 (2013) [15] Kumar, G., & Malik, H. Generalized Regression Neural Network Based Wind Speed Prediction Model for Western Region of India. Procedia Computer Science, 93(September), 26–32. https://doi.org/10.1016/j.procs.07.177 (2016) [16] Sun, G., Hoff, S., Zelle, B., & Nelson, M.Development and Comparison of Backpropagation and Generalized Regression Neural Network Models to Predict Diurnal and Seasonal Gas and PM 10 Concentrations and Emissions from Swine Buildings, 0300(08) (2008). [17] Viloria, A., & Gaitan-Angulo, M. (2016). Statistical Adjustment Module Advanced Optimizer Planner and SAP Generated the Case of a Food Production Company. Indian Journal Of Science And Technology, 9(47). doi:10.17485/ijst/2016/v9i47/107371 [18] Amelec, V., & Alexander, P. (2015). Improvements in the Automatic Distribution Process of Finished Product for Pet Food Category in Multinational Company. Advanced Science Letters, 21(5), 1419-1421. [19] Viloria, A., & Robayo, P. V. (2016). Inventory reduction in the supply chain of finished products for multinational companies. Indian Journal of Science and Technology, 8(1).PublicationORIGINALIntegration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Export Potential of a Company.pdfIntegration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Export Potential of a Company.pdfapplication/pdf602850https://repositorio.cuc.edu.co/bitstreams/d1cb97e1-d7a8-4365-8a8b-e5dd5e3725f7/download16e10d2ad26d442f24ce5b2119e2f5e7MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/b28ed1fd-6c86-4305-9c68-d068e909b35f/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/8fc3cf5d-1777-4605-a31d-67a06f959fc3/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILIntegration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Export Potential of a Company.pdf.jpgIntegration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Export Potential of a Company.pdf.jpgimage/jpeg45150https://repositorio.cuc.edu.co/bitstreams/4af01316-a5c4-4f91-8f18-5535f61f6101/download008bd727f1b835dee11654791ac615f2MD55TEXTIntegration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Export Potential of a Company.pdf.txtIntegration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Export Potential of a Company.pdf.txttext/plain32309https://repositorio.cuc.edu.co/bitstreams/4c7457b8-ae2f-44c3-8d2f-71236b205c96/download21612233825bece118f2de7ba6d8f45fMD5611323/4833oai:repositorio.cuc.edu.co:11323/48332024-09-17 12:48:05.784http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |