This page is devoted to show the supplementary materials for the paper entitled "A contrast pattern-based scientometric study of the QS world university ranking" published in IEEE Access, 2020.

Classification results obtained by all the tested classifiers

  • Classification results by using accuracy as evaluation measure ->

Our collected database.
  • Database including the name of the universities and their positions into the QS ranking [1] ->
  • Database without including the position ->
  • Database without including the name of the universities ->
HTML models for visualizing patterns with up five items
  • HTML models by using Bokeh [2] (they are interactive) ->
Extracted contrast patterns
  • All extracted patterns describing our collected database and the problem's classes by using the HRFm algorithm [3] ->
  • Most used features from the set of extracted patterns ->
  • Most used items from the set of extracted patterns ->

[1] QS Ranking. URL

[2] Bokeh Development Team (2019). Bokeh: Python library for interactive visualization. URL

3] Loyola-González, O. et al. (2017). PBC4cip: A new contrast pattern-based classifier for class imbalance problems. Knowledge-Based Systems, 115, 100–109.