jRank - Command-line ranking generator using Dominance-based Rough Set Approach

jRank is a decision support tool for dealing with multi-criteria choice and ranking problems. It is a command-line Java application, based on java Rough Sets (jRS) library, which implements methods of data analysis provided by the Dominance-based Rough Set Approach and Variable Consistency Dominance-based Rough Set Approaches. Configuration of the application is read from standard Java *.properties text file(s), read on program startup.

jRank employs Dominance-based Rough Set Approach (DRSA) and Variable Consistency Dominance-based Rough Set Approaches (VC-DRSA). For considered learning set of objects A and test set of objects T (which can be the same as A), both loaded from ISF files, the following steps are performed:

  1. creation of (learning) pairwise comparison table (PCT), on the basis of a given reference ranking (weak order) or given pairwise comparisons of some objects from A,
  2. calculation of lower and upper approximations of outranking relation S and non-outranking relation Sc, for PCT created in step 1; approximations are calculated according to DRSA or chosen VC-DRSA,
  3. induction of certain (or possible) decision rules from lower (or upper, respectively) approximations defined in step 2; in order to induce minimal set of rules, VC-DomLEM algorithm is used; it is also possible to use an exhaustive set of rules, without explicit induction of rules (i.e., to use a virtual exhaustive set of rules),
  4. application of decision rules to all pairs of objects from TxT, which yields a preference structure on set T,
  5. exploitation of the preference structure by a chosen ranking method in order to obtain a final ranking (weak order) over T.

For further information please refer to the user's manual (linked below) and readme.txt file from the jRank ZIP archive.

Below is the list of currently available downloads. By downloading the software you accept the license agreement available at IDSS download page.
  • jRank_2016-07-14_14.00.zip - jRank ZIP archive; includes: jRS library JAR, what's new.txt, readme.txt, jRank.info, jRank batch file, and exemplary experiments (1.6 MB)
  • user's manual (last update: 2013-07-18 00:05)
  • http://www.graphviz.org - free graph visualization software, including Gvedit and dotty. Both programs can be used to visualize preference graphs generated by jRank.
  • ranking-tutorial.pdf (last update: 2009-06-10 07:29) - slides covering chosen aspects of the multi-criteria ranking problem

In case you need further assistance, you can contact the author.

Below is the list of publications concerning the methodology used in jRank. You can download this list in one BibTeX file.
  1. M. Szeląg, S. Greco, R. Słowiński, Variable Consistency Dominance-Based Rough Set Approach to Preference Learning in Multicriteria Ranking. Information Sciences, 277, 2014, pp. 525–552.
  2. M. Szeląg, S. Greco, R. Słowiński, Rule-Based Approach to Multicriteria Ranking. [In]: M. Doumpos, E. Grigoroudis (Eds.), Multicriteria Decision Aid and Artificial Intelligence: Links, Theory and Applications. Wiley, 2013, pp. 127-160.
  3. J. Błaszczyński, R. Słowiński, M. Szeląg, Sequential Covering Rule Induction Algorithm for Variable Consistency Rough Set Approaches. Information Sciences, 181, 2011, pp. 987-1002, doi:10.1016/j.ins.2010.10.030. (manuscript)
  4. M. Szeląg, R. Słowiński, S. Greco, J. Błaszczyński, S. Wilk, jRank - Ranking using Dominance-based Rough Set Approach. Research Report RA-07/10, Poznań University of Technology, 2010. (full text)
  5. M. Szeląg, R. Słowiński, J. Błaszczyński, jRank - Ranking using Dominance-based Rough Set Approach. Newsletter of the European Working Group "Multiple Criteria Decision Aiding", Series 3, no. 22, Fall 2010, pp. 13-15. (full newsletter; manuscript)
  6. J. Błaszczyński, R. Słowiński, M. Szeląg, VC-DomLEM: Rule induction algorithm for variable consistency rough set approaches. Research Report RA-07/09, Poznań University of Technology, 2009. (full text)
  7. J. Błaszczyński, S. Greco, R. Słowiński, M. Szeląg, Monotonic Variable Consistency Rough Set Approaches. International Journal of Approximate Reasoning, 50(7), 2009, pp. 979-999.
  8. P. Fortemps, S. Greco, R. Słowiński, Multicriteria decision support using rules that represent rough-graded preference relations. European Journal of Operational Research, 188(1), 2008, pp. 206-223.
  9. J. Błaszczyński, S. Greco, R. Słowiński, M. Szeląg, Monotonic Variable Consistency Rough Set Approaches. [In]: J. Yao, P. Lingras , W. Wu, M. Szczuka, N. J. Cercone, D. Ślęzak (eds.), Rough Sets and Knowledge Technology 2007. Lecture Notes in Artificial Intelligence, vol. 4481, Springer, Berlin Heidelberg, 2007, pp. 126-133.
  10. J. Błaszczyński, S. Greco, R. Słowiński, M. Szeląg, Monotonic Variable Consistency Rough Set Approaches. Research Report RA-010/07, Poznań University of Technology, 2007.
  11. J. Błaszczyński, S. Greco, R. Słowiński, M. Szeląg, On Variable Consistency Dominance-based Rough Set Approaches. [In]: S. Greco, Y. Hata, S. Hirano, M. Inuiguchi, S. Miyamoto, H. S. Nguyen, R. Słowiński (eds.), Rough Sets and Current Trends in Computing 2006. Lecture Notes in Artificial Intelligence, vol. 4259, Springer, Berlin 2006, pp. 191-202.
  12. R. Słowiński, S. Greco, B. Matarazzo, Rough Set Based Decision Support. Chapter 16 [in]: E. K. Burke, G. Kendall (eds.), Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques. Springer, New York, 2005, pp. 475-527.
  13. R. Słowiński, S. Greco, Inducing Robust Decision Rules from Rough Approximations of a Preference Relation. [In]: L. Rutkowski, J. Siekmann, R. Tadeusiewicz, L. A. Zadeh (eds.), Artificial Intelligence and Soft Computing. Lecture Notes in Artificial Intelligence, vol. 3070, Springer, Berlin Heidelberg, 2004, pp. 118-132.
  14. K. Dembczyński, R. Pindur, R. Susmaga, Dominance-based Rough Set Classifier without Induction of Decision Rules. Electronic Notes in Theoretical Computer Science, 82(4), 2003, pp. 84-95.
  15. S. Greco, B. Matarazzo, R. Słowiński, Rough Sets Theory for Multicriteria Decision Analysis. European Journal of Operational Research, 129(1), 2001, pp. 1-47.
  16. S. Greco, B. Matarazzo, R. Słowiński, A. Tsoukias, Exploitation of a Rough Approximation of the Outranking Relation in Multicriteria Choice and Ranking. [In]: Theodor J. Stewart, Robin C. van den Honert, Trends in Multicriteria Decision Making. Lecture Notes in Economics and Mathematical Systems, vol. 465, Springer, Berlin, 1998, pp. 45–60.
  17. D. Bouyssou, P. Vincke, Ranking Alternatives on the Basis of Preference Relations: A Progress Report with Special Emphasis on Outranking Relations. Journal of Multi-Criteria Decision Analysis, vol. 6, 1997, pp. 77-85.
  18. P. Vincke, Exploitation of a crisp relation in a ranking problem. Theory and Decision, vol. 32, 1992, pp. 221-240.