Advertisement

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

  • Arkadiusz WojnaEmail author
  • Rafał Latkowski
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Rough set Discernibility matrix Reduct k nearest neighbors Machine learning Java Weka Distributed computing Open source 

Notes

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.

References

  1. 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. 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_2CrossRefzbMATHGoogle Scholar
  3. 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_5CrossRefGoogle Scholar
  4. 4.
    Brown, F.M.: Boolean Reasoning: The Logic of Boolean Equations. Kluwer Academic Publishers, Dordrecht (1990)CrossRefGoogle Scholar
  5. 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. 6.
    Góra, G., Wojna, A.: RIONA: a new classification system combining rule induction and instance-based learning. Fundamenta Informaticae 51(4), 369–390 (2002)MathSciNetzbMATHGoogle Scholar
  7. 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. 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)CrossRefGoogle Scholar
  9. 9.
    Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11(1), 63–90 (1993)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Hu, Y.C.: Rough sets for pattern classification using pairwise-comparison-based tables. Appl. Math. Model. 37(12–13), 7330–7337 (2013)MathSciNetCrossRefGoogle Scholar
  11. 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_7CrossRefGoogle Scholar
  12. 12.
    Johnson, D.S.: Approximation algorithms for combinatorial problems. J. Comput. Syst. Sci. 9(3), 256–278 (1974)MathSciNetCrossRefGoogle Scholar
  13. 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. 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)zbMATHGoogle Scholar
  15. 15.
    Latkowski, R.: Flexible indiscernibility relations for missing attribute values. Fundamenta Informaticae 67(1–3), 131–147 (2005)MathSciNetzbMATHGoogle Scholar
  16. 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)zbMATHGoogle Scholar
  17. 17.
    Nguyen, H.S.: Discretization of real value attributes: a boolean reasoning approach. Ph.D. thesis, Warsaw University (1997)Google Scholar
  18. 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. 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)zbMATHGoogle Scholar
  20. 20.
    Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)zbMATHGoogle Scholar
  21. 21.
    Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)MathSciNetCrossRefGoogle Scholar
  22. 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/BFb0095102CrossRefGoogle Scholar
  23. 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)CrossRefGoogle Scholar
  24. 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. 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_28CrossRefGoogle Scholar
  26. 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. 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. 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_27CrossRefGoogle Scholar
  29. 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_11CrossRefGoogle Scholar
  30. 30.
    Telembici, T., Grama, L.: Detecting indoor sound events. Acta Technica Napocensis - Electron. Telecommun. 59(2), 13–17 (2018)Google Scholar
  31. 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_19CrossRefGoogle Scholar
  32. 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_53CrossRefGoogle Scholar
  33. 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. 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_11CrossRefGoogle Scholar
  35. 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_13CrossRefGoogle Scholar
  36. 36.
    Wojna, A., Latkowski, R., Kowalski, Ł.: RSESLIB: User Guide. http://rseslib.mimuw.edu.pl/rseslib.pdf
  37. 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_22CrossRefGoogle Scholar
  38. 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. 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_4CrossRefGoogle Scholar
  40. 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_55CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Security On-DemandSan DiegoUSA
  2. 2.Loyalty PartnerWarsawPoland

Personalised recommendations