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Concept Formation : Rough Sets and Scott Systems

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Języki publikacji
EN
Abstrakty
EN
The paper addresses the problem of concept formation (knowledge granulation) in the settings of rough set theory. The original version of rough set theory implicitly accommodates a lot of well-established philosophical assumptions about concept formation as presented by A. Rand. However, as suggested by S. Hawking and L. Mlodinow, one has also to consider the dynamics of the universe of objects and different scales at which concepts may be formed. These both aspects have already been discussed separately in rough set theory. Different forms of dynamics have been addressed explicitly – especially the case of extending the universe by new objects; in contrast, different scales of description have been addressed implicitly, mainly within the Granular Computing (GrC) paradigm. Following the example of Life, the famous game invented by J. Conway, we describe the corresponding dynamics in Pawlak information systems using a GrC driven methodology. Having dynamics discussed, we address the problem of concept formation at zoom-out scales of description. To this end, we build Scott systems as information systems describing the universe at a coarser scale than the original scale of Pawlak systems. We regard these systems as a special type of classifications, which have already been studied in the context of rough sets by A. Skowron et al.
Wydawca
Rocznik
Strony
17--33
Opis fizyczny
Bibliogr. 50 poz., rys.
Twórcy
autor
  • Maria Curie-Skłodowska University, Dept. of Logic and Philosophy of Science, Lublin, Poland
  • Białystok University, Institute of Computer Science, Białystok, Poland
Bibliografia
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  • [10] Hawking, S., Mlodinow, L.: The Grand Design, Bantam Press, 2010.
  • [11] Inuiguchi, M., Hirano, S., Tsumoto, S., Eds.: Rough Set Theory and Granular Computing, Springer-V, Berlin Heidelberg, 2003.
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  • [13] Jankowski, A., Peters, J. F., Skowron, A., Stepaniuk, J.: Optimization in discovery of compound granules, Fundamenta Informaticae, 85(1-4), 2008, 249-265.
  • [14] Nguyen, H. S., Skowron, A., Stepaniuk, J.: Granular computing: A rough set approach, Computational Intelligence, 17(3), 2001, 514-544.
  • [15] Pal, S. K., Polkowski, L., Skowron, A., Eds.: Rough-Neural Computing: Techniques for Computing with Words, Springer-V, Berlin Heidelberg, 2004.
  • [16] Pawlak, Z.: Information systems - theoretical foundations, Information Systems, 6(3), 1981, 205-218.
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  • [20] Pawlak, Z., Skowron, A.: Rough sets and Boolean reasoning, Information Sciences, 177, 2007,41-73.
  • [21] Pawlak, Z., Skowron, A.: Rough sets: Some extensions, Information Sciences, 177, 2007, 28-40.
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  • [24] Pedrycz, W., Skowron, A., Kreinovich, V., Eds.: Handbook of Granular Computing, John Wiley & Sons, Chichester, 2008.
  • [25] Polkowski, L., Skowron, A.: Towards adaptive calculus of granules, in: [49], vol. 1, 1999, 201-228.
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  • [29] Skowron, A.: Toward intelligent systems: Calculi of information granules, Lecture Notes in Artificial Intelligence, 2253, 2001, 251-260.
  • [30] Skowron, A.: Approximation spaces in rough neurocomputing, in: [11], 2003, 13-22.
  • [31] Skowron, A., Stepaniuk, J.: Towards discovery of information granules, Lecture Notes in Artificial Intelligence, 1704, 1999, 542-547.
  • [32] Skowron, A., Stepaniuk, J.: Information granules and rough-neural computing, in: [15], 2004,43-84.
  • [33] Skowron, A., Stepaniuk, J.: Modeling of high quality granules, in: The Int. Conf. RSEISP’2007 Proceedings (M. Kryszkiewicz et al., Ed.), vol. 4585 of Lecture Notes in Artificial Intelligence, Springer-V, Berlin Heidelberg, 2007, 300-309.
  • [34] Skowron, A., Stepaniuk, J.: Rough sets and granular computing: Toward rough-granular computing, in: [24], 2008, 425-448.
  • [35] Skowron, A., Stepaniuk, J., Peters, J. F.: Rough sets and infomorphisms: Towards approximation of relations in distributed environments, Fundamenta Informaticae, 54(2-3), 2003,263-277.
  • [36] Skowron, A., Stepaniuk, J., Peters, J. F., Swiniarski, R.: Calculi of approximation spaces, Fundamenta Informaticae, 72(1-3), 2006, 363-378.
  • [37] Skowron, A., Swiniarski, R., Synak,P.: Approximation spaces and information granulation, Transactions on Rough Sets III: Journal subline of LNCS, 3400, 2005, 175-189.
  • [38] Skowron, A., Szczuka, M.: Toward interactive computations: A rough-granular approach, in: Advances in Machine Learning. Dedicated to the Memory of Professor Ryszard Michalski (J. Koronackiet al., Ed.), vol. 2, Springer-V, Heidelberg, 2009, 23-42.
  • [39] Skowron, A., Wasilewski, P.: Information systems in modeling interactive computations on granules, in: Rough Sets and Current Trends in Computing - The 7th Int. Conf. RSCTC ’2010 Proceedings (M. Szczuka et al., Ed.), vol. 6086 of Lecture Notes in Artificial Intelligence, Springer-V., Berlin Heidelberg, 2010,730-739.
  • [40] Skowron, A., Wasilewski, P.: Information systems in modeling computations on granules, Theoretical Computer Science, 412(42), 2011, 5939-5959.
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  • [42] Stepaniuk, J.: Rough-Granular Computing in Knowledge Discovery and Data Mining, Springer-V., Berlin Heidelberg, 2008.
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  • [44] Szczuka, M., Skowron, A., Stepaniuk, J.: Function approximation and quality measures in rough-granular systems, Fundamenta Informaticae, 109(3), 2011, 339-354.
  • [45] Thomas, W.: Ayn Rand’s theory of concepts: A brief overview, http: / / www. calstatela. edu/faculty/ areed2/ThomasARConcepts.pdf.
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  • [50] Zhang, G. Q., Shen, G.: Approximable concepts, Chu spaces, and information systems, Theory and Applications of Categories, 17(5), 2006, 79-102.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-10de6934-8ab6-4281-9c97-8ec72f4c70ff
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