Name
Title | ||
|---|---|---|
Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines. |
Abstract | ||
|---|---|---|
•Despite successful applications of ensembles, the petroleum industry has not benefited enough.•SVM is promising but its performance depends mostly on the regularization parameter.•We propose an SVM ensemble with diverse opinions on the regularization parameter.•The proposed model outperformed Random Forest but competitive with SVM Bagging.•There is great potential for ensemble models in petroleum reservoir characterization. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.asoc.2014.10.017 | Applied Soft Computing |
Keywords | Field | DocType |
Stacked generalization ensemble,Support vector machines,Regularization parameter,Porosity,Permeability | Data mining,Regularization (mathematics),Artificial intelligence,Random forest,Ensemble learning,Correlation coefficient,Mathematical optimization,Petroleum industry,Ensemble forecasting,Support vector machine,Machine learning,Mathematics,Petroleum reservoir | Journal |
Volume | ISSN | Citations |
26 | 1568-4946 | 6 |
PageRank | References | Authors |
0.47 | 40 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anifowose Fatai | 1 | 47 | 6.04 |
| Jane Labadin | 2 | 44 | 8.64 |
| Abdul-Azeez Abdulraheem | 3 | 51 | 8.75 |