TThis page is devoted to show the supplementary materials for the paper entitled "PBC4cip: A New Contrast Pattern-based Classifier for Class Imbalance Problems" published in Knowledge-Based Systems, pp. 100-109, 2017.

• AUC results ->
• G-Mean results ->

Statistical results using Friedman's test (as a non-parametric test), Wilcoxon Signed Ranks (for pairwise comparison), and Finner's procedure (as a post-hoc procedure) [3]. The post hoc comparisons contain α = 0.05, α = 0.10, and adjusted p-values.

Statistical results for all the tested contrast pattern-based classifiers

• According to the AUC measure ->
• According to the G-Mean measure ->
• Wilcoxon Signed Ranks test between our proposal and SMOTE-TL+LCMine [1]; according to the AUC measure ->
• Wilcoxon Signed Ranks test between our proposal and SMOTE-TL+LCMine [1]; according to the G-Mean measure ->

Statistical results for all the tested classifiers not directly based on contrast patterns

• According to the AUC measure ->
• According to the G-Mean measure ->
• Wilcoxon Signed Ranks test between our proposal and RUSBoost [2]; according to the AUC measure ->
• Wilcoxon Signed Ranks test between our proposal and RUSBoost [2]; according to the G-Mean measure ->

• [New] -> We have implemented PBC4cip as a Python Package ->
• PBC4cip algorithm was implemented on .NET environment. In the PBC4cip.zip file we provide the source code of the PBC4cip algorithm and some examples using ARFF file (WEKA database file) ->
• We have implemented the PBC4cip algorithm as a Weka Package. You can download this version from here. It is important to highlight that our Weka package depends on the package discriminantAnalysis, which can be obtained through Weka's package manager or directly from this website. The discriminantAnalysis package must be installed before the PBC4cip package. If you installed PBC4cip without installing the discriminantAnalysis package, uninstall PBC4cip first.

[1] O. Loyola-González, J. F. Martínez-Trinidad, J. A. Carrasco-Ochoa, and M. García-Borroto, “Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases,” Neurocomputing, vol. 175, no. Part B, pp. 935–947, 2016.

[2] C. Seiffert, T. M. Khoshgoftaar, J. Van Hulse, and A. Napolitano, “RUSBoost: A Hybrid Approach to Alleviating Class Imbalance,” Syst. Man Cybern. Part A Syst. Humans, IEEE Trans., vol. 40, no. 1, pp. 185–197, Jan. 2010.

[3] 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.