Skip to main content
SpringerLink
Log in

Rseslib 3: Library of Rough Set and Machine Learning Methods with Extensible Architecture

  • Chapter
  • First Online:
Transactions on Rough Sets XXI

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 10810))

Abstract

The paper presents a new generation of Rseslib library - a collection of rough set and machine learning algorithms and data structures in Java. It provides algorithms for discretization, discernibility matrix, reducts, decision rules and for other concepts of rough set theory and other data mining methods. The third version was implemented from scratch and in contrast to its predecessor it is available as a separate open-source library with API and with modular architecture aimed at high reusability and substitutability of its components. The new version can be used within Weka and with a dedicated graphical interface. Computations in Rseslib 3 can be also distributed over a network of computers.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://sourceforge.net/projects/modlem.

  2. 2.

    http://users.aber.ac.uk/rkj/?page_id=79.

  3. 3.

    https://archive.ics.uci.edu/ml.

  4. 4.

    https://github.com/wgromniak/mahout-extensions.

  5. 5.

    http://rseslib.mimuw.edu.pl.

  6. 6.

    https://github.com/awojna/Rseslib.

References

  1. Adamczyk, M.: Parallel feature selection algorithm based on rough sets and particle swarm optimization. In: Proceedings of the 2014 Federated Conference on Computer Science and Information System. In: ACSIS, vol. 2, pp. 43–50 (2014)

    Google Scholar 

  2. Bazan, J.G., Szczuka, M.: The rough set exploration system. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 37–56. Springer, Heidelberg (2005). https://doi.org/10.1007/11427834_2

    Chapter  MATH  Google Scholar 

  3. Bazan, J.G., Latkowski, R., Szczuka, M.: DIXER – distributed executor for rough set exploration system. In: Ślęzak, D., Yao, J.T., Peters, J.F., Ziarko, W., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 39–47. Springer, Heidelberg (2005). https://doi.org/10.1007/11548706_5

    Chapter  Google Scholar 

  4. Brown, F.M.: Boolean Reasoning: The Logic of Boolean Equations. Kluwer Academic Publishers, Dordrecht (1990)

    Book  Google Scholar 

  5. Fayyad, U., Irani, K.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1022–1027. Morgan Kaufmann (1993)

    Google Scholar 

  6. Góra, G., Wojna, A.: RIONA: a new classification system combining rule induction and instance-based learning. Fundamenta Informaticae 51(4), 369–390 (2002)

    MathSciNet  MATH  Google Scholar 

  7. Grama, L., Rusu, C.: Choosing an accurate number of mel frequency cepstral coefficients for audio classification purpose. In: Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis, pp. 225–230. IEEE (2017)

    Google Scholar 

  8. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witen, I.: The weka data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)

    Article  Google Scholar 

  9. Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11(1), 63–90 (1993)

    Article  MathSciNet  Google Scholar 

  10. Hu, Y.C.: Rough sets for pattern classification using pairwise-comparison-based tables. Appl. Math. Model. 37(12–13), 7330–7337 (2013)

    Article  MathSciNet  Google Scholar 

  11. Janusz, A., Stawicki, S., Szczuka, M., Ślęzak, D.: Rough set tools for practical data exploration. In: Ciucci, D., Wang, G., Mitra, S., Wu, W.-Z. (eds.) RSKT 2015. LNCS (LNAI), vol. 9436, pp. 77–86. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25754-9_7

    Chapter  Google Scholar 

  12. Johnson, D.S.: Approximation algorithms for combinatorial problems. J. Comput. Syst. Sci. 9(3), 256–278 (1974)

    Article  MathSciNet  Google Scholar 

  13. Kerber, R.: Chimerge: discretization of numeric attributes. In: Proceedings of the 10th National Conference on Artificial Intelligence, pp. 123–128. AAAI Press (1992)

    Google Scholar 

  14. Kryszkiewicz, M.: Properties of incomplete information systems in the framework of rough sets. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methodology and Applications, pp. 422–450. Physica-Verlag, Heidelberg (1998)

    MATH  Google Scholar 

  15. Latkowski, R.: Flexible indiscernibility relations for missing attribute values. Fundamenta Informaticae 67(1–3), 131–147 (2005)

    MathSciNet  MATH  Google Scholar 

  16. Moshkov, M., Piliszczuk, M., Zielosko, B.: Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications. SCI, vol. 145. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  17. Nguyen, H.S.: Discretization of real value attributes: a boolean reasoning approach. Ph.D. thesis, Warsaw University (1997)

    Google Scholar 

  18. Nguyen, H.S., Ślęzak, D.: Approximate reducts and association rules - correspondence and complexity results. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) Proceedings of the International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing. LNCS, pp. 137–145. Springer, Heidelberg (1999)

    Google Scholar 

  19. Øhrn, A., Komorowski, J., Skowron, A., Synak, P.: The design and implementation of a knowledge discovery toolkit based on rough sets - the rosetta system. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, pp. 376–399. Physica-Verlag, Heidelberg (1998)

    MATH  Google Scholar 

  20. Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)

    MATH  Google Scholar 

  21. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)

    Article  MathSciNet  Google Scholar 

  22. Prędki, B., Wilk, S.: Rough set based data exploration using ROSE system. In: Raś, Z.W., Skowron, A. (eds.) ISMIS 1999. LNCS, vol. 1609, pp. 172–180. Springer, Heidelberg (1999). https://doi.org/10.1007/BFb0095102

    Chapter  Google Scholar 

  23. Riza, L.S., et al.: Implementing algorithms of rough set theory and fuzzy rough set theory in the R package “roughsets". Inf. Sci. 287, 68–89 (2014)

    Article  Google Scholar 

  24. Rusu, C., Grama, L.: Recent developments in acoustical signal classification for monitoring. In: Proceedings of the 5th International Symposium on Electrical and Electronics Engineering. IEEE (2017)

    Google Scholar 

  25. Skowron, A.: Boolean reasoning for decision rules generation. In: Komorowski, J., Raś, Z.W. (eds.) ISMIS 1993. LNCS, vol. 689, pp. 295–305. Springer, Heidelberg (1993). https://doi.org/10.1007/3-540-56804-2_28

    Chapter  Google Scholar 

  26. Skowron, A., Grzymała-Busse, J.W.: From rough set theory to evidence theory. In: Yager, R.R., Kacprzyk, J., Fedrizzi, M. (eds.) Advances in the Dempster-Shafer Theory of Evidence, pp. 193–236. Wiley, New York (1994)

    Google Scholar 

  27. Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowinski, R. (ed.) Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  28. Skowron, A., Wojna, A.: K nearest neighbor classification with local induction of the simple value difference metric. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 229–234. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-25929-9_27

    Chapter  Google Scholar 

  29. Stefanowski, J., Tsoukiàs, A.: On the extension of rough sets under incomplete information. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 73–81. Springer, Heidelberg (1999). https://doi.org/10.1007/978-3-540-48061-7_11

    Chapter  Google Scholar 

  30. Telembici, T., Grama, L.: Detecting indoor sound events. Acta Technica Napocensis - Electron. Telecommun. 59(2), 13–17 (2018)

    Google Scholar 

  31. Tiwari, M., Chakrabarti, P., Chakrabarti, T.: Performance analysis and error evaluation towards the liver cancer diagnosis using lazy classifiers for ILPD. In: Zelinka, I., Senkerik, R., Panda, G., Lekshmi Kanthan, P.S. (eds.) ICSCS 2018. CCIS, vol. 837, pp. 161–168. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-1936-5_19

    Chapter  Google Scholar 

  32. Widz, S.: Introducing Nrough framework. In: Polkowski, L., et al. (eds.) IJCRS 2017. LNCS (LNAI), vol. 10313, pp. 669–689. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60837-2_53

    Chapter  Google Scholar 

  33. Wojna, A.: Center-based indexing for nearest neighbors search. In: Proceedings of the 3rd IEEE International Conference on Data Mining, pp. 681–684. IEEE Computer Society Press (2003)

    Google Scholar 

  34. Wojna, A.: Analogy-based reasoning in classifier construction. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets IV. LNCS, vol. 3700, pp. 277–374. Springer, Heidelberg (2005). https://doi.org/10.1007/11574798_11

    Chapter  Google Scholar 

  35. Wojna, A., Latkowski, R.: Rseslib 3: open source library of rough set and machine learning methods. In: Nguyen, H.S., Ha, Q.-T., Li, T., Przybyła-Kasperek, M. (eds.) IJCRS 2018. LNCS (LNAI), vol. 11103, pp. 162–176. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99368-3_13

    Chapter  Google Scholar 

  36. Wojna, A., Latkowski, R., Kowalski, Ł.: RSESLIB: User Guide. http://rseslib.mimuw.edu.pl/rseslib.pdf

  37. Wojnarski, M.: Debellor: a data mining platform with stream architecture. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 405–427. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89876-4_22

    Chapter  Google Scholar 

  38. Wojnarski, M.: Debellor: Open source modular platform for scalable data mining. In: Proceedings of the 17th International Conference on Intelligent Information Systems (2009)

    Google Scholar 

  39. Wojnarski, M., Stawicki, S., Wojnarowski, P.: TunedIT.org: system for automated evaluation of algorithms in repeatable experiments. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.) RSCTC 2010. LNCS (LNAI), vol. 6086, pp. 20–29. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13529-3_4

    Chapter  Google Scholar 

  40. Wróblewski, J.: Covering with reducts - a fast algorithm for rule generation. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 402–407. Springer, Heidelberg (1998). https://doi.org/10.1007/3-540-69115-4_55

    Chapter  Google Scholar 

Download references

Acknowledgment

We would like to thank Professor Andrzej Skowron for his support and mentorship over the project and for his advice on the development and Professor Dominik Ślęzak for his remarks to this paper. It must be emphasized that the library is the result of joint effort of many people and we express our gratitude to all the contributors: Jan Bazan, Rafał Falkowski, Grzegorz Góra, Wiktor Gromniak, Marcin Jałmużna, Łukasz Kosson, Łukasz Kowalski, Michał Kurzydłowski, Łukasz Ligowski, Michał Mikołajczyk, Krzysztof Niemkiewicz, Dariusz Ogórek, Marcin Piliszczuk, Maciej Próchniak, Jakub Sakowicz, Sebastian Stawicki, Cezary Tkaczyk, Witold Wojtyra, Damian Wójcik and Beata Zielosko.

Author information

Authors and Affiliations

  1. Security On-Demand, 12121 Scripps Summit Dr 320, San Diego, CA, 92131, USA

    Arkadiusz Wojna

  2. Loyalty Partner, Złota 59, 00-120, Warsaw, Poland

    Rafał Latkowski

Authors
  1. Arkadiusz Wojna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rafał Latkowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arkadiusz Wojna .

Editor information

Editors and Affiliations

  1. University of Manitoba, Winnipeg, MB, Canada

    James F. Peters

  2. University of Warsaw, Warsaw, Poland

    Andrzej Skowron

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer-Verlag GmbH Germany, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wojna, A., Latkowski, R. (2019). Rseslib 3: Library of Rough Set and Machine Learning Methods with Extensible Architecture. In: Peters, J., Skowron, A. (eds) Transactions on Rough Sets XXI. Lecture Notes in Computer Science(), vol 10810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58768-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-58768-3_7

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-58767-6

  • Online ISBN: 978-3-662-58768-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation