This page is devoted to show the supplementary materials for the paper entitled "Detecting Pneumatic Failures on Temporary Immersion Bioreactors" published in Lecture Notes in Computer Science, pp. 293-302, 2016.
In this paper, we study the use of contrast pattern-based classifiers, designed for class imbalance problems, for creating an understandable and accurate model for detecting pneumatic failures on temporary immersion bioreactors. Our experiments over eight real-world databases show that a decision tree ensemble obtains significantly better AUC results than other tested classifiers.

• AUC results considering all the databases ->

CD diagrams with a statistical comparison of the classification results and statistical results using Friedman's test (as a non-parametric test) and Shaffer static procedure (as a post-hoc procedure) [1]. The post hoc comparisons contain α = 0.05, α = 0.10, and adjusted p-values. ->

All partitions of the imbalanced databases used in this paper (using 5-fold and distribution optimally balanced stratified cross validation (DOB-SCV) [2]) ->

[1] J. Derrac, S. García, D. Molina, and F. Herrera, “A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms,” Swarm Evol. Comput., vol. 1, no. 1, pp. 3–18, 2011.

[2] J. G. Moreno-Torres, J. A. Saez, and F. Herrera, “Study on the Impact of Partition-Induced Dataset Shift on k-Fold Cross-Validation,” Neural Networks Learn. Syst. IEEE Trans., vol. 23, no. 8, pp. 1304–1312, Aug. 2012.