| Title |
Neural Network Ensembles to Determine Growth Multi-classes in Predictive Microbiology |
| Authors |
FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, Chen, Huanhuan , Gutierrez, P. A. , Hervas-Martinez, C. , Yao, Xin |
| External publication |
Si |
| Means |
Lect. Notes Comput. Sci. |
| Scope |
Proceedings Paper |
| Nature |
Científica |
| JCR Quartile |
4 |
| SJR Quartile |
2 |
| SJR Impact |
0.34600 |
| Publication date |
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. |
| Keywords |
Negative Correlation Learning; Neural Networks; Ordinal Regression |
| Universidad Loyola members |
|
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