PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Optimization in Discovery of Compound Granules

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The problem considered in this paper is the evaluation of perception as a means of optimizing various tasks. The solution to this problem hearkens back to early research on rough set theory and approximation. For example, in 1982, Ewa Orowska observed that approximation spaces serve as a formal counterpart of perception. In this paper, the evaluation of perception is at the level of approximation spaces. The quality of an approximation space relative to a given approximated set of objects is a function of the description length of an approximation of the set of objects and the approximation quality of this set. In granular computing (GC), the focus is on discovering granules satisfying selected criteria. These criteria take inspiration from the minimal description length (MDL) principle proposed by Jorma Rissanen in 1983. In this paper, the role of approximation spaces in modeling compound granules satisfying such criteria is discussed. For example, in terms of approximation itself, this paper introduces an approach to function approximation in the context of a reinterpretation of the rough integral originally proposed by Zdzisaw Pawlak in 1993. We also discuss some other examples of compound granule discovery problems that are related to compound granules representing process models and models of interaction between processes or approximation of trajectories of processes. All such granules should be discovered from data and domain knowledge. The contribution of this article is a proposed solution approach to evaluating perception that provides a basis for optimizing various tasks related to discovery of compound granules representing rough integrals, process models, their interaction, or approximation of trajectories of discovered models of processes.
Wydawca
Rocznik
Strony
249--265
Opis fizyczny
bibliogr. 43 poz., wykr.
Twórcy
autor
autor
autor
autor
  • Institute of Decision processes Support & AdgaM Solutions Sp. z.o.o. Wawozowa 9 lok. 64, 02-796 Warsaw, Poland, andrzejj@adgam.com.pl
Bibliografia
  • [1] Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction, Kluwer Academic Publishers, Dordrecht, 2003.
  • [2] Bazan, J., G., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J., J.: Automatic planning of treatment of infants with respiratory failure through rough set modeling. In: Proceedings of RSCTC'2006, LNAI 4259, Springer, Heidelberg, 2006, 418-427.
  • [3] Bazan J., Peters, J., F., Skowron, A.: Behavioral pattern identification through rough set modelling. In: Proceedings of RSFDGrC'2005, LNAI 3641, Springer, Heidelberg 2005, 688-697.
  • [4] Bazan, J., Skowron, A., Swiniarski, R.: Rough sets and vague concept approximation: From sample approximation to adaptive learning. Transactions on Rough Sets V: Journal Subline, LNCS 3100, 2006, 39-63, Springer, Heidelberg.
  • [5] Bazan, J.: Rough sets and granular computing in behavioral pattern identification and planning. In: Pedrycz, W., Skowron, A., Kreinovich, V. (Eds.) [22] (in press).
  • [6] Bronshtein, I.N., Semendyayev, K.A., Musiol, G., Muehlig, H. (eds.): Handbook of Mathematics, 4th Ed.Springer, Berlin, 2003.
  • [7] Goldin, D., Smolka, S., Wegner, P. (2006) Interactive Computation: The New Paradigm. Springer, Heidelberg, 2006.
  • [8] Feng, J., Jost, J., Minping, Q. (eds.): Network: From Biology to Theory. Springer, Berlin, 2007.
  • [9] Ferraty, F. and Vieu, P.: NonParametric Functional Data Analysis: Theory and Practice. Springer, New York, 2006.
  • [10] Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. A. (eds.). Feature Extraction. Foundations and Applications. Springer, Berlin, 2006.
  • [11] James, G. M., Sugar, C.: Clustering for sparsely sampled functional data. Journal of the American Statistical Association 98, 2003, 397-408.
  • [12] Kleinberg, J., Papadimitriou, Ch., Raghavan, P.: A microeconomic view of data mining. Data Mining and Knowledge Discovery 2, 1998, 311-324.
  • [13] Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. Springer, Berlin 1998.
  • [14] Liu, H., Motoda, H.: Feature Extraction, Construction and Selection: A Data Mining Perpective. Springer, Berlin 1998.
  • [15] Luck, M., McBurney, P., Preist, C.: Agent Technology. Enabling Next Generation Computing: A Roadmap for Agent Based Computing. AgentLink, 2003.
  • [16] Mitchell, M.: Complex systems: Network thinking. Artificial Intelligence 170(18), 2006, 1194-1212.
  • [17] Pawlak, Z., Classification of Objects byMeans of Attributes, Institute for Computer Science, Polish Academy of Sciences, Report 429, 1981.
  • [18] Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht 1991.
  • [19] Pawlak, Z.: Rough sets, rough functions and rough calculus. In: S. K. Pal and A. Skowron (eds.), Rough Fuzzy Hybridization, A New Trend in and Decision Making Processes, Springer-Verlag, Singapore 1999, 99-109.
  • [20] Pawlak, Z., Peters, J., Skowron, A., Suraj, Z., Ramanna, S., Borkowski, M.: Rough measures and integrals: A Brief introduction. In: T. Terano, T. Nishida, A. Namatame, S. Tsumoto, Y. Ohsawa and T. Washio (eds.), New Frontiers in Artificial Intelligence, LNAI 2253, Springer-Verlag, Berlin 2001, 374-379.
  • [21] Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177(1) 2007, 3-27; Rough sets: Some extensions. Information Sciences 177(1) 2007, 28-40; Rough sets and Boolean reasoning. Information Sciences 177(1), 2007, 41-73.
  • [22] Pedrycz, W., Skowron, A., Kreinovich, V. (Eds.): Handbook of Granular Computing, John Wiley & Sons, New York (in press).
  • [23] Peters, J. F.: Classification of perceptual objects by means of features. International Journal of Information Technology and Intelligent Computing, 2007, in press.
  • [24] Peters, J. F., Ramanna, S.: Feature selection: Near set approach. In: Z. W. Ras, S. Tsumoto, D. A. Zighed (eds.), 3rd Int. Workshop on Mining Complex Data (MCD'07), ECML/PKDD-2007, LNAI, Springer, 2007, in press.
  • [25] Peters, J. F.: Classification of objects by means of features. In: Proc. IEEE SymposiumSeries on Foundations of Computational Intelligence (IEEE SCCI 2007), Honolulu, Hawaii, 1-5 April, 2007, 1-8.
  • [26] Peters, J.F.: Near sets: Special theory about nearness of objects. Fundamenta Informaticae, vol. 75(1-4), 2007, 407-433.
  • [27] Peters, J. F., Skowron, A., Stepaniuk, J.: Nearness of objects: Extension of approximation space model. Fundamenta Informaticae 79(3-4), 2007, 497-512.
  • [28] J.F. Peters, S. Shahfar, S. Ramanna, T. Szturm, Biologically-inspired adaptive learning: A near set approach, In: Proc. Frontiers in the Convergence of Bioscience and Information Technologies (FBIT07), IEEE, NJ, 11 October 2007, in press.
  • [29] Peters, J. F.: Rough ethology: Towards a biologically-inspired study of collective behavior in intelligent systems with approximation spaces, Transactions on Rough Sets, III, 2005, 153-174.
  • [30] Ramsay, J. O., Silverman, B. W.: Applied Functional Data Analysis. Springer, Berlin, 2002.
  • [31] Ramsay, J. O., Silverman, B. W.: Functional Data Analysis. Springer, Berlin, 2005 (2nd edition).
  • [32] Rissanen, J.: Minimum-description-length principle. In: S. Kotz, N. Johnson (eds.), Encyclopedia of Statistical Sciences, John Wiley & Sons, New York, NY, 1985, 523-527.
  • [33] Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27, 1996, 245-253.
  • [34] Skowron, A., Stepaniuk, J.: Information granules and rough-neural computing. In: Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing: Techniques for Computing with Words, Springer-Verlag, Berlin 2004, 43-84.
  • [35] Skowron, A., Stepaniuk, J., Peters, J. F., Swiniarski, R.: Calculi of approximation spaces. Fundamenta Informaticae 72(1-3), 2006, 363-378.
  • [36] Skowron, A., Synak, P.: Complex patterns. Fundamenta Informaticae 60(1-4), 2004, 351-366.
  • [37] Ślęzak, D.: Approximate entropy reducts. Fundamenta Informaticae 53(3-4), 2002, 365-390.
  • [38] Stepaniuk, J.: Knowledge discovery by application of rough set models. In: L. Polkowski, S. Tsumoto, T.Y. Lin (eds.), Rough SetMethods and Applications.New Developments in KnowledgeDiscovery in Information Systems, Physica-Verlag, Heidelberg 2000, 137-233.
  • [39] Stevens, R., Brook, P., Jackson, K., Arnold, S.: Systems Engineering. Coping with Complexity. Prentice-Hall, London, 1998.
  • [40] Sun, R. (ed.): Cognition and Multi-Agent Interaction. From Cognitive Modeling to Social Simulation. Cambridge University Press, New York, NY, 2006.
  • [41] http://en.wikipedia.org/wiki/Interaction
  • [42] Zadeh, L. A.: A new direction in AI: Toward a computational theory of perceptions. AI Magazine 22(1), 2001, 73-84.
  • [43] Zadeh, L. A.: Generalized theory of uncertainty (GTU)-principal concepts and ideas. Computational Statistics and Data Analysis 51, 2006, 15-46.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS5-0016-0017
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.