Logo PTI Logo FedCSIS

Position Papers of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 31

Application of Random Sampling in the Concept-Dependent Granulation Method


DOI: http://dx.doi.org/10.15439/2022F264

Citation: Position Papers of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 31, pages 311 ()

Full text

Abstract. Professor Zadeh in his works proposed the idea of grouping similar objects on the basis of certain similarity measures, thus initiating the paradigm of granular computing. He made the assumption that similar objects may have similar decisions. This natural assumption, operates in other scientific methodologies, e.g. methods based on k nearest neighbours, in reasoning by analogy and in rough set theory. The above assumption implies the existence of grouped information nodes (granules) and has potential applications in reducing the size of decision systems. The hypothesis has guided, among others, the creation of granulation techniques based on the use of rough inclusions (introduced by Polkowski and Skowron) - according to the scheme proposed by Polkowski. Where the possibility of a large reduction of the size of decision systems while maintaining the classification efficiency was verified in experimental works.In this paper, we investigate the possibility of using random sampling in the approximation of decision systems - as part of dealing with big data sets. We use concept-dependent granulation as a reference approximation method. Experiments on selected real-world data have shown a common regularity that gives a hint on how to apply random sampling for fast and effective size reduction of decision systems.


  1. Toy decision system generator, http://toyds.herokuapp.com/generator/v1/. Last accessed 12 Apr 2022
  2. Artiemjew, P.: (2022). Rough Inclusion Based Toy Decision Systems Generator For Presenting Data Mining Algorithms. Proceedings of the 3rd Polish Conference on Artificial Intelligence, April 25-27, 2022, Gdynia, Poland, 168-171.
  3. Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341—356 (1982). https://doi.org/10.1007/BF01001956
  4. Polkowski, L.: A model of granular computing with applications, In: Proceedings of IEEE 2006 Conference on Granular Computing GrC06, pp. 9–16. IEEE Press, Atlanta, USA (2006)
  5. Polkowski, L., Artiemjew, P.: Granular Computing in Decision Approximation - An Application of Rough Mereology, In: Intelligent Systems Reference Library 77, Springer, ISBN 978-3-319-12879-5, pp. 1–422 (2015).
  6. Artiemjew, P.: Classifiers from Granulated Data Sets: Concept Dependent and Layered Granulation, In: Proceedings RSKD’07. The Workshops at ECML/PKDD’07, pp. 1–9., Warsaw Univ. Press, Warsaw (2007)
  7. Artiemjew, P., Ropiak, K.: ’A Novel Ensemble Model - The Random Granular Reflections’, Fundamenta Informaticae, 1 Jan. 2021, vol. 179, no. 2, pp. 183-203, 2021(http://dx.doi.org/10.3233/FI-2021-2020)
  8. J. Szypulski, P. Artiemjew: The Rough Granular Approach to Classifier Synthesis by Means of SVM, In: Proceedings of International Joint Conference on Rough Sets, IJCRS’15, pp. 256-263, Tianjin, China, Lecture Notes in Computer Science (LNCS), Springer, Heidelberg (2015)
  9. Ropiak, K.; Artiemjew, P. On a Hybridization of Deep Learning and Rough Set Based Granular Computing. Algorithms 2020, 13, 63.
  10. Ropiak K., Artiemjew P. (2020) Random Forests and Homogeneous Granulation. In: Lopata A., Butkiene R., Gudoniene D., Sukacke V. (eds) Information and Software Technologies. ICIST 2020. Communications in Computer and Information Science, vol 1283. Springer, Cham. https://doi.org/10.1007/978-3-030-59506-7_16 (2020)
  11. Artiemjew P., Kislak-Malinowska A. (2019) Using r-indiscernibility Relations to Hide the Presence of Information for the Least Significant Bit Steganography Technique. In: Damaševičius R., Vasiljeviene G. (eds) Information and Software Technologies. ICIST 2019. Communications in Computer and Information Science, vol 1078. Springer
  12. Artiemjew P.: Boosting effect for classifier based on simple granules of knowledge. In: Information Technology And Control (ITC) vol. 47(2), pp. 184-196 (2018)
  13. L. Polkowski, P. Artiemjew: Granular Computing: Classifiers and Missing Values, in Proceedings ICCI’07. 6th IEEE International Conference on Cognitive Informatics, IEEE Computer Society, Los Alamitos, CA, 2007, pp. 186-194.
  14. Artiemjew, P., Ropiak, K.: Robot localization in the magnetic unstable environment, 5th Workshop on Collaboration of Humans, Agents, Robots, Machines and Sensors (CHARMS 2019), The Third IEEE International Conference on Robotic Computing (IRC 2019), Naples, Italy
  15. UCI ML Repository, https://archive.ics.uci.edu/ml/index.php.