On foundations and applications of the paradigm of granular rough computing

International Journal of Cognitive Informatics and Natural Intelligence

Volumn 2, Issue 2, 2008, Pages 80-94

  Polkowski, Lech ^a   Semeniuk Polkowska, Maria ^b  

^a POLISH JAPANESE INSTITUTE OF INFORMATION TECHNOLOGY   (Poland)

^b UNIVERSITY OF WARSAW   (Poland)

Author keywords

Classification; Fusion of knowledge; Granular systems; Granulation of knowledge; Granules of knowledge; Reasoning by granular logics; Rough inclusions; Rough mereology; Rough sets; Similarity

Indexed keywords

APPROXIMATION THEORY; CLASSIFICATION (OF INFORMATION); FORMAL METHODS; GRANULATION; ROUGH SET THEORY;

GRANULAR SYSTEM; GRANULATION OF KNOWLEDGE; GRANULES OF KNOWLEDGE; REASONING BY GRANULAR LOGICS; ROUGH INCLUSIONS; ROUGH MEREOLOGY; SIMILARITY;

COMPUTATION THEORY;

EID: 58149227065     PISSN: 15573958     EISSN: 15573966     Source Type: Journal    
DOI: 10.4018/jcini.2008040105     Document Type: Article

References (49)

1. Bazan, J. G. (1998). A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision tables. In L. Polkowski & A. Skowron (Eds.), Rough sets in knowledge discovery 1 (pp. 321-365). Heidelberg, Germany: Physica-Verlag.
2. Bazan, J. G., Nguyen, H. S., Nguyen, S. H., Synak, P., & Wróblewski, J.(2000). Rough set algorithms in classification problems. In L. Polkowski, S. Tsumoto, & T. Y. Lin (Eds.), Rough set methods and applications (pp. 49-88). Heidelberg, Germany: Physica-Verlag.
3. Chan, C., Kinsner, W., Wang, Y., & Miller, M. (Eds.). (2004, August). Proceedings of the Third IEEE International Conference on Cognitive Informatics (ICCI'04). Los Alamitos, CA: IEEE Computer Society Press.
4. Ha'jek, P. (1998). Metamathematics of fuzzy logic. Dordrecht, Germany: Kluwer.
5. Kinsner, W., Zhang, D., Wang, Y., & Tsai, J. (Eds.). (2005, August). Proceedings of the Fourth IEEE International Conference on Cognitive Informatics (ICCI'05). Los Alamitos, CA: IEEE Computer Society Press.
6. Kloesgen, W., & Żytkow, J. (Eds.). (2002). Handbook of data mining and knowledge discovery. Oxford, England: Oxford University Press.
7. Les̈niewski, S. (1916). Podstawy ogólnej teoryi mnogosci [On the foundations of set theory]. Moscow: The Polish Scientific Circle.
8. Lin, T. Y. (1997). From rough sets and neighborhood systems to information granulation and computing with words. In Proceedings of the European Congress on Intelligent Techniques and Soft Computing (pp. 1602-1606).
9. Lin, T.Y. (2005). Granular computing: Examples, intuitions, and modeling. In Proceedings of IEEE 2006 Conference on Granular Computing GrC06 (pp. 40-44).
10. Nguyen, S. H. (2000). Regularity analysis and its applications in data mining. In L. Polkowski, S. Tsumoto, & T. Y. Lin (Eds.), Rough set methods and applications (pp. 289-378). Heidelberg, Germany: Physica-Verlag.
11. Pal, S. K. (2004). Soft data mining, computational theory of perception and rough-fuzzy approach. Information Sciences, 163(1/3), 5-12.
12. Pal, S. K., Polkowski, L., & Skowron, A. (Eds.). (2004). Rough-neural computing: Techniques for computing with words. Berlin, Germany: Springer Verlag.
13. Patel, D., Patel, S., & Wang, Y. (Eds.). (2003, August). In Proceedings of the Second IEEE International Conference on Cognitive Informatics (ICCI'03. Los Alamitos, CA: IEEE Computer Society Press.
14. Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences, 11, 341-356.
15. Pawlak, Z. (1991). Rough sets: Theoretical aspects of reasoning about data. Dordrecht, Germany: Kluwer.
16. Pawlak, Z., & Skowron, A. (1994). Rough membership functions. In R. R. Yager, M. Fedrizzi, & J. Kasprzyk (Eds.), Advances in the Dempster-Shafer theory of evidence (pp. 251-271). New York: Wiley.
17. Poldrack, R. A., et al. (2001, November 29). Interactive memory systems in the human brain. Nature, 546-50.
18. Polkowski, L. (2002). Rough sets. Mathematical foundations. Heidelberg, Germany: Physica-Verlag.
19. Polkowski, L. (2003). A rough set paradigm for unifying rough set theory and fuzzy set theory (a plenary talk). In Proceedings RSFDGrC03, Chongqing, China (LNAI 2639, pp. 70-78).
20. Polkowski, L. (2004a). A note on 3-valued rough logic accepting decision rules. Fundamenta Informaticae, 61, 37-45.
21. Polkowski, L. (2004b). A rough-neural computation model based on rough mereology. In Pal et al. (Eds.), (pp. 85-108).
22. Polkowski, L. (2004c). Toward rough set foundations. Mereological approach (a plenary talk). In Proceedings of RSCTC04, Uppsala, Sweden (LNAI 3066, pp. 8-25). Berlin, Germany: Springer Verlag.
23. Polkowski, L. (2005a). Formal granular calculi based on rough inclusions (a feature talk). In Proceedings of the IEEE 2005 Conference on Granular Computing (GrC05) (pp. 57-62).
24. Polkowski, L. (2005b). Rough-fuzzy-neurocomputing based on rough mereological calculus of granules. International Journal of Hybrid Intelligent Systems, 2, 91-108.
25. Polkowski, L. (2006). A model of granular computing with applications (a feature talk). In Proceedings of IEEE 2006 Conference on Granular Computing (GrC06) (pp. 9-16).
26. Polkowski, L. (2007). Granulation of knowledge in decision systems: The approach based on rough inclusions. The method and its applications (a plenary talk). In Proceedings of RSEISP 07, Warsaw, Poland (LNAI 4585). Berlin, Germany: Springer Verlag.
27. Polkowski, L., & Artiemjew, P. (2007a). On granular rough computing: Factoring classifiers through granulated structures. In Proceedings of RSEISP07 (LNAI 4585, pp. 280-289). Berlin, Germany: Springer Verlag.
28. Polkowski, L., & Artiemjew, P. (2007b). On granular rough computing with missing values. In Proceedings of RSEISP07 (LNAI 4585, pp. 271-279). Berlin, Germany: Springer Verlag.
29. Polkowski, L., & Semeniuk-Polkowska, M. (2005). On rough set logics based on similarity relations. Fundamenta Informaticae, 64, 379-390.
30. Polkowski, L., & Skowron, A. (1997). Rough mereology: A new paradigm for approximate reasoning. International Journal of Approximate Reasoning, 15(4), 333-365.
31. Polkowski, L., & Skowron, A. (2001). Rough mereological calculi of granules: A rough set approach to computation. Computational Intelligence: An International Journal, 17(3), 472-492.
32. Polkowski, L., & Skowron, A. (1999a). Grammar systems for distributed synthesis of approximate solutions extracted from experience. In Gh. Paun & A. Salomaa (Eds.), Grammatical models of multiagent systems (pp., 316-333). Amsterdam: Gordon & Breach.
33. Polkowski, L., & Skowron, A. (1999b). Towards an adaptive calculus of granules. In L. A. Zadeh & J. Kacprzyk (Eds.), Computing with words in information/intelligent systems,1, (pp. 201-228). Heidelberg, Germany: Physica-Verlag.
34. Qing, L., & Hui, S. (2006). Theoretical study of granular computing. In Proceedings of the RSKT06, Chongqing, China (LNAI 4062, pp. 92-102). Berlin, Germany: Springer Verlag.
35. Shastri, L., & Wendelken, C. (1999). Knowledge fusion in the large-Taking cues from the brain. In Proceedings of FUSION'99.
36. Skowron, A., et al. (1994). RSES: A system for data analysis. Retrieved from www.logic.mimuw.edu.pl/rses/
37. Skowron, A., & Stepaniuk, J. (2001). Information granules: Towards foundations of granular computing. International Journal of Intelligent Systems, 16, 57-85.
38. Skowron, A., & Stepaniuk, J. (2004). Information granules and rough-neural computing. In S. K. Pal et al. (Eds.), (pp. 43-84).
39. Stefanowski, J. (1998). On rough set based approaches to induction of decision rules. In L. Polkowski & A. Skowron (Eds.), Rough sets in knowledge discovery 1, (pp. 500-529). Heidelberg, Germany: Physica-Verlag.
40. Wang, Y. (2007, January-March). The theoretical framework of cognitive informatics. International Journal of Cognitive Informatics and Natural Intelligence, 1(1), 1-27.
41. Wang Y., Johnston, R. H., & Smith, M. R. (Eds.). (2002, August). Proceedings of the First IEEE International Conference on Cognitive Informatics (ICCI'02), Calgary, Canada. Los Alamitos, CA: IEEE Computer Society Press.
42. Wojna, A. (2005). Analogy-based reasoning in classifier construction. Transactions on Rough Sets IV (LNCS 3700, pp. 277-374). Berlin, Germany: Springer Verlag.
43. Wroblewski, J. (2000). Adaptive aspects of combining approximation space. In S. K. Pal et al. (Eds.), (2004) (pp. 139-156).
44. Yao, Y. Y. (2000). Granular computing: Basic issues and possible solutions. In Proceedings of the Fifth Joint Conference Information Sciences I, (pp. 186-189). Atlantic, NJ: Assoc. Intell. Machinery.
45. Yao, Y. Y. (2004). Information granulation and approximation in a decision-theoretic model of rough sets. In S. K. Pal et al. (Eds.), (2004), (pp. 491-516).
46. Yao, Y. Y. (2005). Perspectives of granular computing. In Proceedings of the IEEE 2005 Conference on Granular Computing (GrC05) (pp. 85-90).
47. Yao, Y. Y., Shi, Z., Wang, Y., & Kinsner, W. (Eds.). (2006, July). Proceedings of the Fifth IEEE International Conference on Cognitive Informatics (ICCI'06), Beijing, China. Los Alamitos CA: IEEE Computer Society Press.
48. Zadeh, L. A. (1979). Fuzzy sets and information granularity. In M. Gupta, R. Ragade, & R. Yager (Eds.), Advances in fuzzy set theory and applications (pp. 3-18). Amsterdam: North Holland.
49. Zadeh, L. A. (2005). Graduation and granulation are keys to computation with information described in natural language. In Proceedings of the IEEE 2005 Conference on Granular Computing (GrC05) (p. 30).