This page is devoted to show the supplementary materials for the paper entitled "Effect of Class Imbalance on Quality Measures for Contrast Patterns: An Experimental Study" published in Information Science, pp. 179–192, 2016.

AUC Results
  • AUC results obtained by the LCMine miner [1] jointly with the CAEP classifier [2], using the pattern selection algorithms. ->

Statistical Tests
  • CD diagrams with a statistical comparison of the classification results. ->
  • Statistical results using Friedman's test (as a non-parametric test) and Shaffer static procedure (as a post-hoc procedure) [3]. The post hoc comparisons contain α = 0.05, α = 0.10, and adjusted p-values. ->

[1] M. García-Borroto, J. F. Martínez-Trinidad, J. A. Carrasco-Ochoa, M. A. Medina-Pérez, and J. Ruiz-Shulcloper, “LCMine: An efficient algorithm for mining discriminative regularities and its application in supervised classification”, Pattern Recognit., vol. 43, no. 9, pp. 3025–3034, 2010.

[2] G. Dong, X. Zhang, L. Wong, and J. Li, “CAEP: Classification by Aggregating Emerging Patterns,” in Discovery Science, vol. 1721, S. Arikawa and K. Furukawa, Eds. Springer Berlin Heidelberg, 1999, pp. 30–42.

[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.