@inproceedings{a9e926903f9d4c1ab8328b7bbba07470,
title = "Neural network ensembles to determine growth multi-classes in predictive microbiology",
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. {\textcopyright} 2012 Springer-Verlag.",
keywords = "Negative Correlation Learning, Neural Networks, Ordinal Regression",
author = "F. FERN{\'A}NDEZ-NAVARRO and Huanhuan CHEN and P.A. GUTI{\'E}RREZ and C. HERV{\'A}S-MART{\'I}NEZ and Xin YAO",
year = "2012",
doi = "10.1007/978-3-642-28931-6_30",
language = "English",
isbn = "9783642289309",
series = "Lecture Notes in Computer Science",
publisher = "Springer Berlin Heidelberg",
pages = "308--318",
editor = "Emilio CORCHADO and V{\'a}clav SN{\'A}{\v S}EL and Ajith ABRAHAM and Micha{\l} WO{\'Z}NIAK and Manuel GRA{\~N}A and Sung-Bae CHO",
booktitle = "Hybrid Artificial Intelligent Systems : 7th International Conference, HAIS 2012, Salamanca, Spain, March 28-30th, 2012, Proceedings, Part II",
note = "7th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2012 ; Conference date: 28-03-2012 Through 30-03-2012",
}