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An Approach to Pattern Recognition Based on Hierarchical Granular Computing

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Języki publikacji
EN
Abstrakty
EN
This paper summarizes the some of the recent developments in the area of application of rough sets and granular computing in hierarchical learning. We present the general framework of rough set based hierarchical learning. In particular, we investigate several strategies of choosing the appropriate learning algorithms for first level concepts as well as the learning methods for the intermediate concepts. We also propose some techniques for embedding the domain knowledge into the granular, layered learning process in order to improve the quality of hierarchical classifiers. This idea, which has been envisioned and developed by professor Andrzej Skowron over the last 10 years, shows to be very efficient in many practical applications. Throughout the article, we illustrate the proposed methodology with three case studies in the area of pattern recognition. The studies demonstrate the viability of this approach for such problems as: sunspot classification, hand-written digit recognition, and car identification.
Wydawca
Rocznik
Strony
369--384
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
autor
  • Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008, Warsaw, Poland
autor
  • Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008, Warsaw, Poland
autor
  • Faculty of Mathematics, Informatics and Mechanics, The University of Warsaw, Banacha 2, 02-097 Warszawa, Poland
autor
  • Faculty of Mathematics, Informatics and Mechanics, The University of Warsaw, Banacha 2, 02-097 Warszawa, Poland
Bibliografia
  • [1] Jan G. Bazan, Hung Son Nguyen, and Marcin Szczuka. A view on rough set concept approximations. Fundamenta Informaticae, 59(2-3):107-118, 2004.
  • [2] Jan G. Bazan and Marcin S. Szczuka. RSES and RSESlib - a collection of tools for rough set computations. In Ziarko and Yao [27], pages 106-113.
  • [3] Gilles Celeux and Grard Govaert. A classification EM algorithm for clustering and two stochastic versions. Computational Statistics & Data Analysis, 14(3):315-332, 1992.
  • [4] J. Geist, R. A. Wilkinson, S. Janet, P. J. Grother, B. Hammond, N. W. Larsen, R. M. Klear, C. J. C. Burges, R. Creecy, J. J. Hull, T. P. Vogl, and C. L. Wilson. The second census optical character recognition systems conference. NIST Technical Report NISTIR 5452, National Institute of Standards and Technology, 1994.
  • [5] Masahiro Inuiguchi, Shusaku Tsumoto, and Shoji Hirano, editors. Rough Set Theory and Granular Computing, volume 125 of Studies in Fuzziness and Soft Computing, Heidelberg, 2003. Springer.
  • [6] Willi Kloesgen and Jan Zytkow, editors. Handbook of Knowledge Discovery and Data Mining. Oxford University Press, Oxford, 2002.
  • [7] Patrick S. McIntosh. The classification of sunspot groups. Solar Physics, 125:251-267, 1990.
  • [8] Hung Son Nguyen, Andrzej Skowron, and Marcin Szczuka. Situation identification by Unmanned Aerial Vehicle. In Ziarko and Yao [27], pages 49-56.
  • [9] Hung Son Nguyen and Marcin Szczuka. Analysis of image sequences for the unmanned aerial vehicle. In Inuiguchi et al. [5], pages 291-300.
  • [10] Sinh Hoa Nguyen and Hung Son Nguyen. Rough sets and granular computing in hierarchical learning. In Pedrycz et al. [14], pages 801-822.
  • [11] Tuan Trung Nguyen. Adaptive classifier construction: An approach to handwritten digit recognition. In James J. Alpigini, James F. Peters, Jacek Skowronek, and Ning Zhong, editors, Rough Sets and Current Trends in Computing, Third International Conference, RSCTC 2002, Malvern, PA, USA, October 14-16, 2002, Proceedings, volume 2475 of Lecture Notes in Computer Science, pages 578-585. Springer, 2002.
  • [12] L. S. Oliveira, R. Sabourin, F. Bortolozzi, and C. Y. Suen. Feature selection using multi-objective genetic algorithms for handwritten digit recognition. In Proceedings of International Conference on Pattern Recognition (ICPR02), pages 568-571. IEEE Computer Society, 2002.
  • [13] Sankar K. Pal, Lech Polkowski, and Andrzej Skowron, editors. Rough-Neural Computing: Techniques for Computing with Words. Cognitive Technologies. Springer, Berlin Heidelberg, 2004.
  • [14] Witold Pedrycz, Andrzej Skowron, and Vladik Kreinovich, editors. Handbook of Granular Computing. John Wiley & Sons, Chichester, 2008.
  • [15] Lech Polkowski and Andrzej Skowron. Rough mereology: A new paradigm for approximate reasoning. International Journal of Approximate Reasoning, 15(4):333-365, 1996.
  • [16] Lech Polkowski and Andrzej Skowron. Towards adaptive calculus of granules. In Lotfi A. Zadeh and Janusz Kacprzyk, editors, Computing with Words in Information/Intelligent Systems, volume 33 of Studies in Fuzziness and Soft Computing, pages 201-227. Physica-Verlag, Heidelberg, 1999.
  • [17] Lech Polkowski and Andrzej Skowron. Rough mereological calculi of granules: A rough set approach to computation. Computational Intelligence, 17(3):472-492, 2001.
  • [18] Andrzej Skowron. Approximate reasoning by agents in distributed environments - invited lecture. In Ning Zhong, Jiming Liu, Setsuo Ohsuga, and Jeffrey Bradshaw, editors, Proceedings of the Second Pacific-Asia Conference on Intelligent Agent Technology, pages 28-39, Singapore, 2001. World Scientific.
  • [19] Andrzej Skowron. Approximation spaces in rough neurocomputing. In Inuiguchi et al. [5], pages 13-22.
  • [20] Andrzej Skowron and Jarosław Stepaniuk. Information granule decomposition. Fundamenta Informaticae, 47(3-4):337-350, 2001.
  • [21] Andrzej Skowron and Jarosław Stepaniuk. Information granules: Towards foundations of granular computing. International Journal of Intelligent Systems, 16(1):57-85, 2001.
  • [22] Andrzej Skowron and Jarosław Stepaniuk. Information granules and rough-neural computing. In Pal et al. [13], pages 43-84.
  • [23] Andrzej Skowron and Marcin Szczuka. Approximate reasoning schemes: Classifiers for computing with words. In P. Grzegorzewski, O. Hryniewicz, and M.A. Gil, editors, Proceedings of the Soft Methods in Probability, Statistics and Data Analysis Conference (SMPS2002), Advances in Soft Computing, pages 338345, Heidelberg, 2002. Physica Verlag.
  • [24] Peter Stone. Layered Learning in Multi-Agent Systems: A Winning Approach to Robotic Soccer. MIT Press, Cambridge, MA, 2000.
  • [25] Lotfi A. Zadeh. Fuzzy logic = computing with words. IEEE Transactions on Fuzzy Systems, 4(2):103-111, 1996.
  • [26] Lotfi A. Zadeh. A new direction in AI: Toward a computational theory of perceptions. AI Magazine, 22(1):73- 84, 2001.
  • [27] Wojciech Ziarko and Y. Y. Yao, editors. Rough Sets and Current Trends in Computing, Second International Conference, RSCTC 2000 Banff, Canada, October 16-19, 2000, Revised Papers, volume 2005 of Lecture Notes in Computer Science. Springer, 2001.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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