CADABRA

CADABRA - CAse-based reasoning DominAnce-Based Rough Set Approach

CADABRA is a GUI Java application supporting Case-based Reasoning. To this end, it uses Dominance-based Rough Set Approach. The resulting methodology is called CBR-DRSA.


The application is developed in the Laboratory of Intelligent Decision Support Systems. It is written using Eclipse RCP (Reach Client Platform), with computational code implemented in the general purpose jRS (java Rough Sets) library (used as a plugin). This library implements methods of data analysis provided by the Dominance-based Rough Set Approach and Variable Consistency Dominance-based Rough Set Approaches.


The application gives the user a possibility to manually define different similarity function for each condition attribute present in the considered data set. Similarity functions serve as condition criteria, while membership functions to considered fuzzy sets serve as decision criteria. The user can also arbitrary choose so called "reference objects" (i.e., known cases), which guide data analysis. Implemented computational methods allow calculation of lower and upper approximations of upward and downward alpha-cuts of considered fuzzy sets, induction of decision rules according to sequential covering method by means of VC-DomLEM algorithm, and computation of membership degree to each considered fuzzy set for new (i.e., previously unseen) objects. This computation is done by a VC-DRSA classifier, using certain and possible decision rules, induced from upward and downward alpha-cuts of considered fuzzy sets.


People involved in the CADABRA project:

  • Prof. Roman Słowiński - project supervisor,

  • M.Sc. Marcin Szeląg - design, programming, further development and maintenance of the application,

  • M.Sc. Jerzy Błaszczyński - design, consultation,

  • B.Sc. students: Ewa Łuczak, Aleksandra Mazurek, Łukasz Tomankiewicz, and Konrad Łykowski - first version of the application, developed for B.Sc. thesis: Application of the Dominance-based Rough Set Approach to Case-based Reasoning, Poznań, 2008.


Below is the list of currently available downloads. By downloading the software you accept the IDSS license agreement.

  • cadabra-2009.02.04_13.25.zip - Windows executables (21 MB)

  • data-2009.02.04_13.25.zip - exemplary data files in ISF format (10 kB)

  • CBR-DRSA.pdf - tutorial for CBR-DRSA methodology

  • documentation.pdf - user manual (in preparation; in the meantime, you can always contact one of the authors in case you need more detailed information)


Below is the list of publications concerning CBR-DRSA methodology. You can download this list in one BibTeX file.

  1. M. Szeląg, S. Greco, R. Słowiński, Similarity-Based Classification with Dominance-Based Decision Rules. [In]: V. Flores et al. (Eds.): Rough Sets, International Joint Conference, IJCRS 2016, Santiago de Chile, Chile, October 7–11, 2016, Proceedings. Lecture Notes in Artificial Intelligence, vol. 9920, Springer, 2016, pp. 355–364. (manuscript)

  2. M. Szeląg, S. Greco, J. Błaszczyński, R. Słowiński, Case-based Reasoning using Dominance-based Decision Rules. [In]: J.T. Yao et al. (Eds), Rough Sets and Knowledge Technology 2011. Lecture Notes in Computer Science, vol. 6954, Springer, Heidelberg, 2011, pp. 404-413.

  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. 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)

  5. S. Greco, B. Matarazzo, R. Słowiński, Granular Computing for Reasoning About Ordered Data: the Dominance-Based Rough Set Approach. Chapter 15 [in]: W. Pedrycz, A. Skowron, V. Kreinovich (eds.), Handbook of Granular Computing. John Wiley & Sons, Chichester, 2008, pp. 347-373.

  6. S. Greco, B. Matarazzo, R. Słowiński, Case-based reasoning using gradual rules induced from dominance-based rough approximations. [In]: G. Wang, T. Li, J. W. Grzymała-Busse, D. Miao, A. Skowron, Y. Yao (eds.), Rough Sets and Knowledge Technology (RSKT 2008). Lecture Notes in Artificial Intelligence, vol. 5009, Springer, Berlin, 2008, pp. 268-275.

  7. J. Błaszczyński, S. Greco, R. Słowiński, Multi-criteria classification - A new scheme for application of dominance-based decision rules. European Journal of Operational Research, 181(3), 2007, pp. 1030-1044.

  8. S. Greco, B. Matarazzo, R. Słowiński, Dominance-based Rough Set Approach to Case-Based Reasoning. [In]: V. Torra, Y. Narukawa, A. Valls, J. Domingo-Ferrer (eds.), Modelling Decisions for Artificial Intelligence. Lecture Notes in Artificial Intelligence, vol. 3885, Springer, Berlin Heidelberg, 2006, pp. 7-18.