Neural network ensembles to determine growth multi-classes in predictive microbiology

Authors:
F. Fernández-Navarro;Huanhuan Chen;P. A. Gutiérrez;C. Hervás-Martínez;Xin Yao
Affiliations:
Department of Computer Science and Numerical Analysis, University of Cordoba, Córdoba, Spain;The Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, University of Birmingham, UK;Department of Computer Science and Numerical Analysis, University of Cordoba, Córdoba, Spain;Department of Computer Science and Numerical Analysis, University of Cordoba, Córdoba, Spain;The Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, University of Birmingham, UK
Venue:
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Year:
2012

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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.