| Título |
Neural Network Ensembles to Determine Growth Multi-classes in Predictive Microbiology |
| Autores |
FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, Chen, Huanhuan , Gutierrez, P. A. , Hervas-Martinez, C. , Yao, Xin |
| Publicación externa |
Si |
| Medio |
Lect. Notes Comput. Sci. |
| Alcance |
Proceedings Paper |
| Naturaleza |
Científica |
| Cuartil JCR |
4 |
| Cuartil SJR |
2 |
| Impacto SJR |
0.34600 |
| Fecha de publicacion |
01/01/2012 |
| ISI |
000309173800030 |
| Abstract |
This paper evaluates the performance of different ordinal regression, nominal classifiers and regression models when predicting probability growth of the Staphylococcus Aureus microorganism. The prediction problem has been formulated as an ordinal regression problem, where the different classes are associated to four values in an ordinal scale. The results obtained in this paper present the Negative Correlation Learning as the best tested model for this task. In addition, the use of the intrinsic ordering information of the problem is shown to improve model performance. |
| Palabras clave |
Negative Correlation Learning; Neural Networks; Ordinal Regression |
| Miembros de la Universidad Loyola |
|
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