ruleRank and RUDE are decision support tools for dealing with multi-criteria choice and ranking problems. They are 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.
rulRank is a command-line Java application, configured using standard Java *.properties text file(s), read on program startup. It reads two input ISF files, containing learning and test information tables, and uses supplied preference information to produce a series of output files: induced decision rules, preference graph, and final ranking of objects from the test information table.
RUDE is a GUI JavaFX application, offering addition functionality:
edition of information tables,
edition of experiment configuration (including edition of preference information),
visualization of obtained results (induced decision rules, preference graph, final ranking, etc.).
This application has been implemented by Piotr Jówko, as part of his master thesis defended in October 2018 (under the supervision of Ph.D. Marcin Szeląg). It requires Java 10+ and JavaFX to run. If you use Java 11 or higher, you need to additionally install JavaFX - see more at https://openjfx.io and download JavaFX at https://gluonhq.com/products/javafx.
Both programs are compatible - they can read the same experiment configuration files and the same input ISF files; they also produce the same output files.
ruleRank and RUDE employ Dominance-based Rough Set Approach (DRSA) and Variable Consistency Dominance-based Rough Set Approaches (VC-DRSA). They enable to configure and run ranking experiments. In each experiment, 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:
creation of (learning) pairwise comparison table (PCT), on the basis of a given reference ranking (weak order) or given pairwise comparisons of reference objects from A,
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,
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),
application of decision rules to all pairs of objects from TxT, which yields a preference structure on set T,
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 PDF manuals linked below and readme.txt files from ZIP archives linked below.
Below is the list of currently available downloads for ruleRank. By downloading the software you accept the IDSS license agreement.
ruleRank_2018-03-29_23.30.zip - ruleRank ZIP archive; includes: jRS library JAR, what's new.txt, readme.txt, ruleRank.info, ruleRank batch file, and exemplary experiments (1.7 MB)
user's manual (last update: 2013-07-18 00:05)
www.graphviz.org - free graph visualization software, including Gvedit and dotty. Both programs can be used to visualize preference graphs generated by ruleRank.
ranking-tutorial.pdf (last update: 2009-06-10 07:29) - slides covering chosen aspects of the multi-criteria ranking problem
Below is the list of currently available downloads for RUDE. By downloading the software you accept this license agreement.
RuleRankUltimateDesktopEdition_2018.09.26.zip - RUDE ZIP archive; includes: jRS library JAR, README.txt, launching scripts, manual, license, and exemplary experiments (4.2 MB)
user's manual (last update: 2018-10-14)
In case you need further assistance, you can contact me by e-mail.
Below is the list of publications concerning the metodology used in ruleRank and RUDE. You can download this list in one BibTeX file.
M. Szeląg, Application of the Dominance-based Rough Set Approach to Ranking and Similarity-based Classification Problems, Ph.D. thesis, Poznań University of Technology, 2015 (supervisor: prof. R. Słowiński). (full text; obszerne streszczenie)
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.
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.
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)
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)
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)
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)
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.
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.
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.
J. Błaszczyński, S. Greco, R. Słowiński, M. Szeląg, Monotonic Variable Consistency Rough Set Approaches. Research Report RA-010/07, Poznań University of Technology, 2007.
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.
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.
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.
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.
S. Greco, B. Matarazzo, R. Słowiński, Rough Sets Theory for Multicriteria Decision Analysis. European Journal of Operational Research, 129(1), 2001, pp. 1-47.
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.
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.
P. Vincke, Exploitation of a crisp relation in a ranking problem. Theory and Decision, vol. 32, 1992, pp. 221-240.