RuLeStudio is a decision support tool for training and validation of monotonic decision rule models induced in multi-criteria ordinal classification problems (also known as sorting). Is is based on the ruleLearn library, which contains up-to-date implementations of methods of data analysis provided by the Dominance-based Rough Set Approach and Variable Consistency Dominance-based Rough Set Approaches.
RuLeStudio is a client-server application. Server is written in Java and incorporates ruleLearn library. Client is a single page JavaScript application, written using React.js framework, that can be run in a HMTL5 compliant web browser.
This application has been implemented by bechelor students: Tomasz Dzięcioł, Dominik Szmyt, and Michał Zimny, as part of their bechelor thesis defended in May 2020 (under the supervision of Ph.D. Marcin Szeląg).
RuLeStudio accepts the following input files:
JSON file with metadata (attributes),
JSON file with training/test objects,
CSV file with training/test objects,
XML file with decision rules in RuleML format (with or without characteristics),
ZIP file with serialized project (storing all results of previous calculations).
For further information please refer to the user documentation built into the client application and also available as a separate pdf file.
RuLeStudio project is hosted on GitHub (Apache-2.0 license):
https://github.com/marcin-szelag/rule-studio - extended source code,
https://github.com/dominieq/rule-studio - original source code,
Most recent releases of RuLeStudio, incorporating my extensions to the project, can be downloaded from:
In case you need further assistance, you can contact me by e-mail.
Below is the list of publications concerning the methodology used in RuLeStudio.
M. Szeląg, J. Błaszczyński, R. Słowiński, Rough Set Analysis of Classification Data with Missing Values. [In]: L. Polkowski et al. (Eds.): Rough Sets, International Joint Conference, IJCRS 2017, Olsztyn, Poland, July 3–7, 2017, Proceedings, Part I. Lecture Notes in Artificial Intelligence, vol. 10313, Springer, 2017, pp. 552–565. (manuscript)
J. Błaszczyński, R. Słowiński, M. Szeląg, Induction of Ordinal Classification Rules from Incomplete Data. [In]: J.T. Yao et al. (Eds.): Rough Sets and Current Trends in Computing 2012. Lecture Notes in Artificial Intelligence, vol. 7413, Springer, Berlin Heidelberg, 2012, pp. 56–65.
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)
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.
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.
S. Greco, B. Matarazzo, R. Słowiński, Rough Sets Theory for Multicriteria Decision Analysis. European Journal of Operational Research, 129(1), 2001, pp. 1-47.