FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, Chen, Huanhuan , Gutierrez, P. A. , Hervas-Martinez, C. , Yao, Xin

Si

Lect. Notes Comput. Sci.

Proceedings Paper

Científica

✗

✗

0.346

01/01/2012

000309173800030

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.

Negative Correlation Learning; Neural Networks; Ordinal Regression