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
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