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 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.

• 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: http://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