Integration of data mining classification techniques and ensemble learning for predicting the export potential of a company
Artículo de revista
2019
Procedia Computer Science
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.
- Artículos científicos [3156]
Descripción:
Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Export Potential of a Company.pdf
Título: Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Export Potential of a Company.pdf
Tamaño: 588.7Kb
PDFLEER EN FLIP
Título: Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Export Potential of a Company.pdf
Tamaño: 588.7Kb
PDFLEER EN FLIP
The following license files are associated with this item: