Skip to main content
SpringerLink
Log in

Hierarchical Classifiers for Complex Spatio-temporal Concepts

  • Chapter
Transactions on Rough Sets IX

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 5390))

Abstract

The aim of the paper is to present rough set methods of constructing hierarchical classifiers for approximation of complex concepts. Classifiers are constructed on the basis of experimental data sets and domain knowledge that are mainly represented by concept ontology. Information systems, decision tables and decision rules are basic tools for modeling and constructing such classifiers. The general methodology presented here is applied to approximate spatial complex concepts and spatio-temporal complex concepts defined for (un)structured complex objects, to identify the behavioral patterns of complex objects, and to the automated behavior planning for such objects when the states of objects are represented by spatio-temporal concepts requiring approximation. We describe the results of computer experiments performed on real-life data sets from a vehicular traffic simulator and on medical data concerning the infant respiratory failure.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Murray, J.A., Bradley, H., Craigie, W., Onions, C.: The Oxford English Dictionary. Oxford University Press, Oxford (1933)

    Google Scholar 

  2. Devlin, K.: Logic and Information. Cambridge University Press, Cambridge (1991)

    MATH  Google Scholar 

  3. Joseph, H.W.B.: An Introduction to Logic. Clarendon Press, Oxford (1916)

    MATH  Google Scholar 

  4. Ogden, C.K., Richards, I.A.: The Meaning of Meaning. A Study of the Influence of Language Upon Thought and of the Science of Symbolism. Harcourt, Brace and Company, New York (1923)

    Google Scholar 

  5. Mendelson, E.: Introduction to Mathematical Logic. International Thomson Publishing (1987)

    Google Scholar 

  6. Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning: Data Mining, Inference and Prediction, vol. I-V. Springer, Heidelberg (2001)

    MATH  Google Scholar 

  7. Gabbay, D.M., Hogger, C.J., Robinson, J.A. (eds.): Handbook of Logic in Artificial Intelligence and Logic Programming, vol. I-V. Oxford University Press, New York (1994)

    MATH  Google Scholar 

  8. Ignizio, J.P.: An Introduction to Expert Systems. McGraw-Hill, New York (1991)

    Google Scholar 

  9. Kloesgen, E., Zytkow, J. (eds.): Handbook of Knowledge Discovery and Data Mining. Oxford University Press, Oxford (2002)

    Google Scholar 

  10. Michalski, R., et al. (eds.): Machine Learning, vol. I-IV. Morgan Kaufmann, Los Altos (1983, 1986, 1990, 1994)

    Google Scholar 

  11. Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine learning, neural and statistical classification. Ellis Horwood Limited, England (1994)

    MATH  Google Scholar 

  12. Mitchel, T.M.: Machine Learning. McGraw-Hill, Boston (1997)

    Google Scholar 

  13. Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)

    Book  MATH  Google Scholar 

  14. Bazan, J.G., Szczuka, M.: The Rough Set Exploration System. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 37–56. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  15. RSES: Project web site, http://logic.mimuw.edu.pl/~rses

  16. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. D: System Theory, Knowledge Engineering and Problem Solving, vol. 9. Kluwer Academic Publishers, Dordrecht (1991)

    Book  MATH  Google Scholar 

  17. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177, 3–27 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  18. Peters, J.F., Skowron, A.: Zdzisław Pawlak life and work (1926–2006). Information Sciences 177, 1–2 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  19. Brown, E.M.: Boolean Reasoning. Kluwer Academic Publishers, Dordrecht (1990)

    Book  MATH  Google Scholar 

  20. Pawlak, Z., Skowron, A.: Rough sets and boolean reasoning. Information Sciences 177, 41–73 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  21. Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Słowiński, R. (ed.) Intelligent Decision Support - Handbook of Applications and Advances of the Rough Sets Theory. D: System Theory, Knowledge Engineering and Problem Solving, vol. 11, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  22. Borrett, S.R., Bridewell, W., Langley, P., Arrigo, K.R.: A method for representing and developing process models. Ecological Complexity 4, 1–12 (2007)

    Article  Google Scholar 

  23. Bridewell, W., Langley, P., Todorovski, L., Dzeroski, S.: Inductive process modeling. Machine Learning (to appear, 2008)

    Google Scholar 

  24. Langley, P.: Cognitive architectures and general intelligent systems. AI Magazine 27, 33–44 (2006)

    Google Scholar 

  25. Langley, P., Cummings, K., Shapiro, D.: Hierarchical skills and cognitive architectures. In: Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society, Chicago, IL, pp. 779–784 (2004)

    Google Scholar 

  26. Langley, P., Laird, J.E.: Cognitive architectures: Research issues and challenges. Technical report, Institute for the Study of Learning and Expertise, Palo Alto, CA (2002)

    Google Scholar 

  27. Langley, P., Laird, J.E., Rogers, S.: Cognitive architectures: Research issues and challenges. Technical report, Computational Learning Laboratory, CSLI, Stanford University, Palo Alto, CA (2006)

    Google Scholar 

  28. Langley, P., Shiran, O., Shrager, J., Todorovski, L., Pohorille, A.: Constructing explanatory process models from biological data and knowledge. AI in Medicine 37, 191–201 (2006)

    Google Scholar 

  29. Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing: Techniques for Computing with Words. Cognitive Technologies. Springer, Heidelberg (2003)

    Google Scholar 

  30. Pancerz, K., Suraj, Z.: Discovering concurrent models from data tables with the ROSECON system. Fundamenta Informaticae 60, 251–268 (2004)

    MATH  Google Scholar 

  31. Pancerz, K., Suraj, Z.: Discovery of asynchronous concurrent models from experimental tables. Fundamenta Informaticae 61(2), 97–116 (2004)

    MathSciNet  MATH  Google Scholar 

  32. Pancerz, K., Suraj, Z.: Rough sets for discovering concurrent system models from data tables. In: Hassanien, A.E., Suraj, Z., Ślȩzak, D., Lingras, P. (eds.) Rough Computing: Theories, Technologies and Applications. Idea Group, Inc. (2007)

    Google Scholar 

  33. Pat, L., George, D., Bay, S., Saito, K.: Robust induction of process models from time-series data. In: Fawcett, T., Mishra, N. (eds.) Proceedings of the Twentieth International Conference on Machine Learning, Washington, D.C, pp. 432–439. AAAI Press, Menlo Park (2003)

    Google Scholar 

  34. Skowron, A., Suraj, Z.: Discovery of concurrent data models from experimental tables: A rough set approach. In: Fayyad, U.M., Uthurusamy, R. (eds.) Proceedings of the First International Conference on Knowledge Discovery and Databases Mining (KDD 1995), pp. 288–293. AAAI Press, Menlo Park (1995)

    Google Scholar 

  35. Soar: Project web site, http://sitemaker.umich.edu/soar/home

  36. Suraj, Z.: Discovery of concurrent data models from experimental tables. Fundamenta Informaticae 28, 353–376 (1996)

    MathSciNet  MATH  Google Scholar 

  37. Suraj, Z.: The synthesis problem of concurrent systems specified by dynamic information systems. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 2. Applications, Case Studies and Software Systems. Studies in Fuzziness and Soft Computing, pp. 418–448. Physica-Verlag, Heidelberg (1998)

    Chapter  Google Scholar 

  38. Suraj, Z.: Rough set methods for the synthesis and analysis of concurrent processes. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough set methods and applications: new developments in knowledge discovery in information systems. Studies in Fuzziness and Soft Computing, pp. 379–488. Physica-Verlag, Heidelberg (2000)

    Chapter  Google Scholar 

  39. Suraj, Z.: Discovering concurrent data models and decision algorithms from data: A rough set approach. International Journal on Artificial Intelligence and Machine Learning IRSI, 51–56 (2004)

    Google Scholar 

  40. Unnikrishnan, K.P., Ramakrishnan, N., Sastry, P.S., Uthurusamy, R.: Service-oriented science: Scaling escience impact. In: Proceedings of the Fourth KDD Workshop on Temporal Data Mining: Network Reconstruction from Dynamic Data, The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data (KDD 2006), Philadelphia, USA, August 20-23 (2006)

    Google Scholar 

  41. Breiman, L.: Statistical modeling: the two cultures. Statistical Science 16(3), 199–231 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  42. Poggio, T., Smale, S.: The mathematics of learning: Dealing with data. Notices of the American Mathematical Society (AMS) 5, 537–544 (2003)

    MathSciNet  MATH  Google Scholar 

  43. Vapnik, V. (ed.): Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

  44. Zadeh, L.A.: From computing with numbers to computing with words – from manipulation of measurements to manipulation of perceptions. IEEE Transactions on Circuits and Systems – I: Fundamental Theory and Applications 1, 105–119 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  45. Zadeh, L.A.: A new direction in AI: Toward a computational theory of perceptions. AI Magazine 1, 73–84 (2004)

    Google Scholar 

  46. Zadeh, L.A.: Toward a generalized theory of uncertainty (GTU) - an outline. Information Sciences 171, 1–40 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  47. Ambroszkiewicz, S., Bartyna, W., Faderewski, M., Terlikowski, G.: An architecture of multirobot system based on software agents and the SOA paradigm. In: Czaja, L. (ed.) Proceedings of the Workshop on Concurrency, Specification, and Programming (CS&P 2007), Łagów, Poland, Warsaw, Poland, Warsaw University, September 27–29, pp. 21–32 (2007)

    Google Scholar 

  48. Domingos, P.: Toward knowledge-rich data mining. Data Mining and Knowledge Discovery 1, 21–28 (2007)

    Article  MathSciNet  Google Scholar 

  49. Foster, I.T.: Service-oriented science: Scaling escience impact. In: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Hong Kong, China, December 18-22. IEEE Computer Society, Los Alamitos (2006)

    Google Scholar 

  50. Kriegel, H.P., Borgwardt, K.M., Kröger, P., Pryakhin, A., Schubert, M., Zimek, A.: Future trends in data mining. Data Mining and Knowledge Discovery 1, 87–97 (2007)

    Article  MathSciNet  Google Scholar 

  51. Kuipers, B.: The spatial semantic hierarchy. Artificial Intelligence 119, 191–233 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  52. Stone, P., Sridharan, M., Stronger, D., Kuhlmann, G., Kohl, N., Fidelman, P., Jong, N.K.: From pixels to multi-robot decision-making: A study in uncertainty. Robotics and Autonomous Systems 54, 933–943 (2006)

    Article  Google Scholar 

  53. Guarino, N.: Formal ontology and information systems. In: Proceedings of the First International Conference on Formal Ontology in Information Systems (FOIS 1998), Trento, Italy, June 6-8, pp. 3–15. IOS Press, Amsterdam (1998)

    Google Scholar 

  54. Jarrar, M.: Towards Methodological Principles for Ontology Engineering. Ph.D thesis, Vrije Universiteit Brussel (2005)

    Google Scholar 

  55. Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.A. (eds.): Feature Extraction Foundations and Applications. Studies in Fuzziness and Soft Computing, vol. 207. Springer-Verlag, Berlin (2006)

    Google Scholar 

  56. Liu, H., Motoda, H. (eds.): Feature Extraction, Construction and Selection: A Data Mining Perspective. The Springer International Series in Engineering and Computer Science, vol. 453. Springer, Berlin (1998)

    MATH  Google Scholar 

  57. Liu, H., Motoda, H. (eds.): Feature Selection for Knowledge Discovery and Data Mining. The Springer International Series in Engineering and Computer Science, vol. 454. Springer, Berlin (1998)

    MATH  Google Scholar 

  58. Dejong, G., Mooney, R.: Explanation-based learning: An alternative view. Machine Learning 1, 145–176 (1986)

    Google Scholar 

  59. Ellman, T.: Explanation-based learning: a survey of programs and perspectives. ACM Computing Surveys 21, 163–221 (1989)

    Article  Google Scholar 

  60. Mitchell, T.M., Keller, R.M., Kedar-Cabelli, S.T.: Explanation-based generalization: A unifying view. Machine Learning 1, 47–80 (1986)

    Google Scholar 

  61. Mitchell, T.M., Thrun, S.B.: Learning analytically and inductively. In: Steier, D.M., Mitchell, T.M. (eds.) Mind Matters: A Tribute to Allen Newell, pp. 85–110. Lawrence Erlbaum Associates, Inc., Mahwah (1996)

    Google Scholar 

  62. Penczek, W., Pólrola, A.: Advances in Verification of Time Petri Nets and Timed Automata: A Temporal Logic Approach. Studies in Computational Intelligence. Springer, Secaucus (2006)

    Book  MATH  Google Scholar 

  63. Roddick, J.F., Hornsby, K., Spiliopoulou, M.: An updated bibliography of temporal, spatial and spatio-temporal data mining research. In: Roddick, J.F., Hornsby, K. (eds.) TSDM 2000. LNCS, vol. 2007, pp. 147–163. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  64. Roddick, J.F., Hornsby, K., Spiliopoulou, M.: Yabtsstdmr - yet another bibliography of temporal, spatial and spatio-temporal data mining research. In: Unnikrishnan, K.P., Uthurusamy, R. (eds.) SIGKDD Temporal Data Mining Workshop. ACM Press, pp. 167–175. Springer, San Francisco (2001)

    Google Scholar 

  65. Ichise, R., Shapiro, D., Langley, P.: Learning hierarchical skills from observation. In: Lange, S., Satoh, K., Smith, C.H. (eds.) DS 2002. LNCS, vol. 2534, pp. 247–258. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  66. Makar, R., Mahadevan, S., Ghavamzadeh, M.: Hierarchical multi-agent reinforcement learning. In: Müller, J.P., Andre, E., Sen, S., Frasson, C. (eds.) Proceedings of the Fifth International Conference on Autonomous Agents, Montreal, Canada, pp. 246–253. ACM Press, New York (2001)

    Chapter  Google Scholar 

  67. Paine, R.W., Tani, J.: Motor primitive and sequence self-organization in a hierarchical recurrent neural network. Neural Networks 17, 1291–1309 (2004)

    Article  Google Scholar 

  68. Zhang, L., Zhang, B.: Hierarchical machine learning - a learning methodology inspired by human intelligence. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS, vol. 4062, pp. 28–30. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  69. Paine, R.W., Tani, J.: How hierarchical control self-organizes in artificial adaptive systems. Adaptive Behavior 13, 211–225 (2005)

    Article  Google Scholar 

  70. Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory and Practice. Elsevier, Morgan Kaufmann, CA (2004)

    MATH  Google Scholar 

  71. Haslum, P.: Modern AI planning: Reading list, http://www.ida.liu.se/~pahas/maip/reading.ps

  72. ICAPS 2006: Proceedings of the Sixteenth International Conference on Automated Planning & Scheduling, The English Lake District, Cumbria, UK, June 6-10. AAAI Press, Menlo Park (2006)

    Google Scholar 

  73. LaValle, S.M.: Planning Algorithms. Cambridge University Press, New York (2006)

    Book  MATH  Google Scholar 

  74. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, New Jersey (2003)

    MATH  Google Scholar 

  75. Vlahavas, I., Vrakas, D. (eds.): Intelligent Techniques for Planning. Idea Group Publishing, New York (2004)

    Google Scholar 

  76. Wezel, W.V., Jorna, R., Meystel, A.: Planning in Intelligent Systems: Aspects, Motivations, and Methods. John Wiley & Sons, Hoboken, New Jersey (2006)

    Book  MATH  Google Scholar 

  77. Bazan, J.G., Skowron, A., Peters, J.F., Synak, P.: Spatio-temporal approximate reasoning over complex objects. Fundamenta Informaticae 67, 249–269 (2005)

    MathSciNet  MATH  Google Scholar 

  78. Bazan, J.G., Skowron, A., Stepaniuk, J.: Modelling complex patterns by information systems. Fundamenta Informaticae 67, 203–217 (2005)

    MathSciNet  MATH  Google Scholar 

  79. Marcus, S.: The paradox of the heap of grains, in respect to roughness, fuzziness and negligibility. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS, vol. 1424, pp. 19–23. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  80. Nguyen, S.H., Nguyen, H.S.: Rough set approach to approximation of concepts from taxonomy. In: Proceedings of Knowledge Discovery and Ontologies Workshop (KDO 2004) at ECML/PKDD 2004, Pisa, Italy, University of Pisa, September 24, pp. 13–24 (2004)

    Google Scholar 

  81. Nguyen, S.H., Nguyen, T.T., Nguyen, H.S.: Rough set approach to sunspot classification problem. In: Ślȩzak, D., et al. (eds.) RSFDGrC 2005. LNCS, vol. 3642, pp. 263–272. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  82. Orłowska, E.: Semantics of vague concepts. In: Dorn, G., Weingartner, P. (eds.) Foundation of Logic and Linguistics, pp. 465–482. Plenum Press, New York (1984)

    Google Scholar 

  83. Skowron, A.: Rough sets and vague concepts. Fundamenta Informaticae 64, 417–431 (2005)

    MathSciNet  MATH  Google Scholar 

  84. Skowron, A., Stepaniuk, J.: Hierarchical modelling in searching for complex patterns: Constrained sums of information systems. Journal of Experimental and Theoretical AI 17, 83–102 (2005)

    Article  MATH  Google Scholar 

  85. Skowron, A., Synak, P.: Complex patterns in spatio-temporal reasoning. In: Czaja, L. (ed.) Proceedings Workshop on Concurrency, Specification, and Programming (CS&P 2003), Czarna, Poland, September 25-27, vol. 2, pp. 487–499 (2003)

    Google Scholar 

  86. Skowron, A., Synak, P.: Complex patterns. Fundamenta Informaticae 60, 351–366 (2004)

    MathSciNet  MATH  Google Scholar 

  87. Zadeh, L.A.: Selected papers. In: Yager, R.R., Ovchinnokov, S., Tong, R., Nguyen, H. (eds.) Fuzzy Sets and Applications: Selected Papers by L.A. Zadeh. John Wiley & Sons, New York (1987)

    Google Scholar 

  88. Bazan, J.G.: Rough sets and granular computing in behavioral pattern identification and planning. In: Pedrycz, W., Skowron, A., Kreinovich, V. (eds.) Handbook of Granular Computing, pp. 777–799. John Wiley & Sons, Chichester (2008)

    Google Scholar 

  89. Doherty, P., Łukaszewicz, W., Skowron, A., Szałas, A.: Knowledge Engineering: A Rough Set Approach. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  90. Nguyen, H.S., Skowron, A., Stepaniuk, J.: Granular computing: A rough set approach. Computational Intelligence 17, 514–544 (2001)

    Article  MathSciNet  Google Scholar 

  91. Skowron, A.: Towards granular multi-agent systems. In: Pal, S.K., Ghosh, A. (eds.) Soft Computing Approach to Pattern Recognition and Image Processing, pp. 215–234. World Scientific, Singapore (2002)

    Google Scholar 

  92. Bar-Yam, Y.: Dynamics of Complex Systems. Addison Wesley, New York (1997)

    MATH  Google Scholar 

  93. Desai, A.: Adaptive complex enterprises. Communications ACM 5, 32–35 (2005)

    Article  Google Scholar 

  94. Gell-Mann, M.: The Quark and the Jaguar. Freeman and Co., New York (1994)

    MATH  Google Scholar 

  95. Luck, M., McBurney, P., Shehory, O., Willmott, S.: Agent technology: Computing as interaction. A roadmap for agent-based computing. Agentlink iii, the european coordination action for agent-based computing, University of Southampton, UK (2005)

    Google Scholar 

  96. Sun, R.: Cognition and Multi-Agent Interaction. From Cognitive Modeling to Social Simulation. Cambridge University Press, New York (2006)

    Google Scholar 

  97. Urmson, C., et al.: High speed navigation of unrehearsed terrain: Red team technology for grand challenge. Report CMU-RI-TR-04-37, The Robotics Institute, Carnegie Mellon University (2004)

    Google Scholar 

  98. Hoen, P.J., Tuyls, K., Panait, L., Luke, S., Poutré, J.A.L.: Overview of cooperative and competitive multiagent learning. In: Tuyls, K., Hoen, P.J., Verbeeck, K., Luke, S. (eds.) LAMAS 2005. LNCS, vol. 3898, pp. 1–46. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  99. Liu, J.: Autonomous Agents and Multi-Agent Systems: Explorations in Learning, Self-Organization and Adaptive Computation. World Scientific Publishing, Singapore (2001)

    Book  Google Scholar 

  100. Liu, J., Jin, X., Tsui, K.: Autonomy Oriented Computing: From Problem Solving to Complex Systems Modeling. Kluwer/Springer, Heidelberg (2005)

    MATH  Google Scholar 

  101. Luck, M., McBurney, P., Preist, C.: Agent technology: Enabling next generation. a roadmap for agent based computing. Agentlink, University of Southampton, UK (2003)

    Google Scholar 

  102. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm intelligence. From natural to artificial systems. In: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI 1999). Oxford University Press, UK (1999)

    Google Scholar 

  103. Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Monada, F., Stutzle, T. (eds.): ANTS 2004. LNCS, vol. 3172. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  104. Peters, J.F.: Rough ethology: Towards a biologically-inspired study of collective behavior in intelligent systems with approximation spaces. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 153–174. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  105. Gruber, T.R.: A translation approach to portable ontologies. Knowledge Acquisition 5, 199–220 (1993)

    Article  Google Scholar 

  106. Smith, B.: Ontology and information systems. Technical report, The Buffalo Ontology Site (2001), http://ontology.buffalo.edu/ontologyPIC.pdf

  107. W3C: OWL Web Ontology Language, use cases and requirements, W3C recommendation. Technical report, The World Wide Web Consortium Technical Report, http://www.w3.org/TR/2004/REC-webont-req-20040210/ (2004)

  108. Bradbrook, K., Winstanley, G., Glasspool, D., Fox, J., Griffiths, R.: AI planning technology as a component of computerised clinical practice guidelines. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds.) AIME 2005. LNCS, vol. 3581. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  109. Dojat, M.: Systemes cognitifs pur le traitement d’informations clinques ou biologiques. Habilitation Thesis. L’universite Joseph Fourier, Grenoble, France (1999) (in French)

    Google Scholar 

  110. Dojat, M., Pachet, F., Guessoum, Z., Touchard, D., Harf, A., Brochard, L.: Neoganesh: A working system for the automated control of assisted ventilation in icus. Arificial Intelligence in Medicine 11, 97–117 (1997)

    Article  Google Scholar 

  111. Glasspool, D., Fox, J., Castillo, F.D., Monaghan, V.E.L.: Interactive decision support for medical planning. In: Dojat, M., Keravnou, E.T., Barahona, P. (eds.) AIME 2003. LNCS, vol. 2780, pp. 335–339. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  112. Miksch, S.: Plan management in the medical domain. AI Communications 12, 209–235 (1999)

    Google Scholar 

  113. Miller, R.: Medical diagnostic decision support systems - past, present, and future. Journal of the American Medical Informatics Association 1, 8–27 (1994)

    Article  Google Scholar 

  114. Nilsson, M., Funk, P., Olsson, E.M.G., von Scheele, B., Xiong, N.: Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system. Arificial Intelligence in Medicine 36, 159–176 (2006)

    Article  Google Scholar 

  115. OpenClinical, http://www.openclinical.org

  116. Peek, N.: Decision-theoretic reasoning in medicine: bringing out the diagnosis. In: Proceedings of the Thirteenth Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2001), pp. 203–210 (2001)

    Google Scholar 

  117. Sacchi, L., Verduijn, M., Peek, N., de Jonge, E., de Mol, B., Bellazzi, R.: Describing and modeling time series based on qualitative temporal abstraction. In: Peek, N., Combi, C. (eds.) Working notes of the workshop on Intelligent Data Analysis in bioMedicine and Pharmacology (IDAMAP 2006), pp. 31–36 (2006)

    Google Scholar 

  118. Shahar, Y., Combi, C.: Temporal reasoning and temporal data maintenance: Issues and challenges. Computers in Biology and Medicine 27, 353–368 (2005)

    Google Scholar 

  119. Spyropoulos, C.D.: AI planning and scheduling in the medical hospital environment. Arificial Intelligence in Medicine 20, 101–111 (2000)

    Article  Google Scholar 

  120. Stacey, M., McGregor, C.: Temporal abstraction in intelligent clinical data analysis: A survey. Arificial Intelligence in Medicine 39, 1–24 (2007)

    Article  Google Scholar 

  121. Verduijn, M., Sacchi, L., Peek, N., Bellazzi, R., de Jonge, E., de Mol, B.: Temporal abstraction for feature extraction: A comparative case study in prediction from intensive care monitoring data. Arificial Intelligence in Medicine 41, 1–12 (2007)

    Article  Google Scholar 

  122. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)

    Google Scholar 

  123. Peters, J.F.: Approximation spaces in off-policy monte carlo learning. In: Burczynski, T., Cholewa, W., Moczulski, W. (eds.) Recent Methods in Artificial Intelligence Methods. AI-METH Series, Gliwice, Poland, pp. 139–144 (2005)

    Google Scholar 

  124. Peters, J.F., Henry, C.: Reinforcement learning with approximation spaces. Fundamenta Informaticae 71, 323–349 (2006)

    MathSciNet  MATH  Google Scholar 

  125. Kleinberg, J., Papadimitriou, C., Raghavan, P.: A microeconomic view of data mining. Data Mining and Knowledge Discovery 2, 311–324 (1998)

    Article  Google Scholar 

  126. Keefe, R.: Theories of Vagueness. Cambridge University Press, New York (2000)

    Google Scholar 

  127. Keefe, R., Smith, P.: Vagueness: A Reader. MIT Press, Massachusetts (1997)

    Google Scholar 

  128. Read, S.: Thinking about Logic: An Introduction to the Philosophy of Logic. Oxford University Press, New York (1994)

    Google Scholar 

  129. Zadeh, L.: The concept of linguistic variable and its application to approximate reasoning-I. Information Sciences 8, 199–249 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  130. Zadeh, L.: The concept of linguistic variable and its application to approximate reasoning-II. Information Sciences 8, 301–357 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  131. Zadeh, L.: The concept of linguistic variable and its application to approximate reasoning-III. Information Sciences 9, 43–80 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  132. Dynamics, N.: Special issue on modelling and control of intelligent transportation systems (9 articles). Journal Nonlinear Dynamics 49 (2006)

    Google Scholar 

  133. Peters, J.F.: Approximation spaces for hierarchical intelligent behavioral system models. In: Dunin-Keplicz, B., Jankowski, A., Skowron, A., Szczuka, M. (eds.) Monitoring, Security, and Rescue Techniques in Multiagent Systems. Advances in Soft Computing, pp. 13–30. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  134. Peters, J.F., Henry, C., Ramanna, S.: Rough ethograms: Study of intelligent system behavior. In: Klopotek, M.A., Wierzchon, S.T., Trojanowski, K. (eds.) Proceedings of the International Conference on Intelligent Information Processing and Web Mining (IIS 2005), Gdańsk, Poland, June 13-16, pp. 117–126 (2005)

    Google Scholar 

  135. Birattari, M., Caro, G.D., Dorigo, M.: Toward the formal foundation of ant programming. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 188–201. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  136. Sukthankar, G., Sycara, K.: A cost minimization approach to human behavior recognition. In: Proceedings of Fourth International Joint Conference on Autonomous Agents and Multi-Agent Systems (2005)

    Google Scholar 

  137. Sukthankar, G., Sycara, K.: Automatic recognition of human team behaviors. In: Proceedings of Modeling Others from Observations (MOO), Workshop at the International Joint Conference on Artificial Intelligence (IJCAI 2005) (July 2005)

    Google Scholar 

  138. Adam, N.R., Janeja, V.P., Atluri, V.: Neighborhood based detection of anomalies in high dimensional spatio-temporal sensor datasets. In: Proceedings of the, ACM symposium on Applied computing (SAC 2004), pp. 576–583. ACM Press, New York (2004)

    Chapter  Google Scholar 

  139. Hofmeyr, S.A., Forrest, S., Somayaji, A.: Intrusion detection using sequence of system calls. Journal of Computer Security 2, 151–180 (1998)

    Article  Google Scholar 

  140. Lazarevic, A., Ertoz, L., Kumar, V., Ozgur, A., Srivastava, J.: A comparative study of anomaly detection schemes in network intrusion detection. In: Proceedings of the Third SIAM International Conference on Data Mining, San Francisco, CA, USA, May 1-3. SIAM, Philadelphia (2003)

    Google Scholar 

  141. Li, X., Han, J., Kim, S., Gonzalez, H.: Roam: Rule- and motif-based anomaly detection in massive moving object data sets. In: Proceedings of the Seventh SIAM International Conference on Data Mining, Minneapolis, Minnesota, USA, April 26-28. SIAM, Philadelphia (2007)

    Google Scholar 

  142. Mahoney, M., Chan, P.K.: Learning rules for anomaly detection of hostile network traffic. In: Proceedings of the Third IEEE International Conference on Data Mining (ICDM 2003), Melbourne, Florida, USA, December 19-22, pp. 601–604. IEEE Computer Society, Los Alamitos (2003)

    Chapter  Google Scholar 

  143. Sekar, R., Bendre, M., Dhurjati, D., Bollineni, P.: A fast automaton-based method for detecting anomalous program behaviors. In: Proceedings of the 2001 IEEE Symposium on Security and Privacy (SP 2001), Washington, DC, USA, pp. 144–155. IEEE Computer Society Press, Los Alamitos (2001)

    Chapter  Google Scholar 

  144. Shavlik, J.W., Shavlik, M.: Selection, combination, and evaluation of effective software sensors for detecting abnormal computer usage. In: Kim, W., Kohavi, R., Gehrke, J., DuMouchel, W. (eds.) Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA, August 22-25, pp. 276–285 (2004)

    Google Scholar 

  145. Flach, P.A., Lachiche, N.: Naive bayesian classification of structured data. Machine Learning 57, 233–269 (2004)

    Article  MATH  Google Scholar 

  146. Grandidier, F., Sabourin, R., Suen, C.Y.: Integration of contextual information in handwriting recognition systems. In: Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003), Washington, DC, USA, p. 1252. IEEE Computer Society Press, Los Alamitos (2003)

    Chapter  Google Scholar 

  147. Guo, G., Li, S., Chan, K.: Face recognition by support vector machines. In: Proceedings of the International Conferences on Automatic Face and Gesture Recognition, pp. 196–201 (2000)

    Google Scholar 

  148. Li, Z.C., Suen, C.Y., Guo, J.: Analysis and recognition of alphanumeric handprints by parts. In: Proceedings of the ICPR 1992, pp. 338–341 (1992)

    Google Scholar 

  149. Li, Z.C., Suen, C.Y., Guo, J.: A regional decomposition method for recognizing handprinted characters. SMC 25, 998–1010 (1995)

    Google Scholar 

  150. Nguyen, T.T.: Understanding domain knowledge: Concept approximation using rough mereology. In: Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2005), Washington, DC, USA, pp. 217–222. IEEE Computer Society, Los Alamitos (2005)

    Chapter  Google Scholar 

  151. Pavlidis, T.: Structural Pattern Recognition. Springer, Heidelberg (1980)

    MATH  Google Scholar 

  152. Yousfi, K., Ambroise, C., Cocquerez, J.P., Chevelu, J.: Supervised learning for guiding hierarchy construction: Application to osteo-articular medical images database. In: Proceedings of the 18th International Conference on Pattern Recognition (ICPR 2006), Washington, DC, USA, pp. 484–487. IEEE Computer Society Press, Los Alamitos (2006)

    Chapter  Google Scholar 

  153. WITAS, http://www.ida.liu.se/ext/witas/eng.html

  154. Hoare, C.A.R.: Process algebra: A unifying approach. In: Abdallah, A.E., Jones, C.B., Sanders, J.W. (eds.) Communicating Sequential Processes. LNCS, vol. 3525. Springer, Heidelberg (2005)

    Google Scholar 

  155. Jensen, K.: Coloured Petri Nets: Basic Concepts, Analysis Methods and Practical Use. Springer, New York (2006)

    Google Scholar 

  156. Roscoe, A.W., Hoare, C.A.R., Bird, R.: The Theory and Practice of Concurrency. Prentice Hall, New York (1997)

    Google Scholar 

  157. Suraj, Z.: Tools for generating concurrent models specified by information systems. In: Lin, T.Y., Wildberger, A.M. (eds.) Soft Computing: Rough Sets, Fuzzy Logic, Neural Networks, Uncertainty Management, Knowledge Discovery, pp. 107–110. Society For Computer Simulation, San Diego (1995)

    Google Scholar 

  158. Taubenfeld, G.: Synchronization Algorithms and Concurrent Programming. Prentice-Hall, New York (2006)

    Google Scholar 

  159. Winskel, G., Nielsen, M.: Models for concurrency. In: Handbook of logic in computer science: semantic modelling, vol. 4, pp. 1–148. Oxford University Press, Oxford (1995)

    Google Scholar 

  160. Kauppinen, T., Hyvonen, E.: Modeling and reasoning about changes in ontology time series. In: Kishore, R., Ramesh, R., Sharman, R. (eds.) Ontologies: A Handbook of Principles, Concepts and Applications in Information Systems, pp. 319–338. Springer, New York (2007)

    Chapter  Google Scholar 

  161. Voigt, K.: Reasoning about changes and uncertainty in browser customization. In: Proceedings of the AAAI Fall Symposium, Working Notes of AI Applications to Knowledge Navigation and Retrieval (1995)

    Google Scholar 

  162. Bar-Hillel, A., Hertz, T., Shental, N., Weinshall, D.: Learning a mahalanobis metric from equivalence constraints. Journal of Machine Learning Research 6, 937–965 (2005)

    MathSciNet  MATH  Google Scholar 

  163. Dzeroski, S., Lavrac, N. (eds.): Relational Data Mining. Springer, New York (2001)

    MATH  Google Scholar 

  164. Gartner, T.: A survey of kernels for structured data. SIGKDD Explorations 5, 48–58 (2003)

    Article  Google Scholar 

  165. Nguyen, S.H.: Regularity Analysis and Its Applications in Data Mining. PhD thesis, Warsaw University, Warsaw, Poland (2000)

    Google Scholar 

  166. Shalev-Shwartz, S., Singer, Y., Ng, A.Y.: Online and batch learning of pseudo-metrics. In: Proceedings of the twenty-first international conference on Machine learning (ICML 2004), vol. 4585. ACM Press, New York (2004)

    Google Scholar 

  167. Weinberger, K., Blitzer, J., Saul, L. (eds.): Distance metric learning for large margin nearest neighbor classification. Advances in Neural Information Processing Systems, vol. 18. MIT Press, Cambridge (2006)

    MATH  Google Scholar 

  168. Wettschereck, D., Aha, D.W., Mohri, T.: A review and empirical evaluation of feature weighting methods for aclass of lazy learning algorithms. Artifficial Intelligence Review 11, 273–314 (1997)

    Article  Google Scholar 

  169. Wojna, A.: Analogy-based Reasoning in Classifier Construction. PhD thesis, Warsaw University; Faculty of Mathematics, Informatics and Mechanics, Warsaw, Poland (2004); Defense in 2005. Awarded with honours

    Google Scholar 

  170. Xing, E.P., Ng, A.Y., Jordan, M.I., Russell, S. (eds.): Distance metric learning with application to clustering with side-information. Advances in NIPS, vol. 15. MIT Press, Cambridge (2003)

    Google Scholar 

  171. Yang, L., Jin, R., Sukthankar, R., Liu, Y.: An efficient algorithm for local distance metric learning. In: Proceedings of the Twenty-first National Conference on Artificial Intelligence (AAAI 2006). ACM Press, New York (2006)

    Google Scholar 

  172. Bazan, J.G., Skowron, A.: Classifiers based on approximate reasoning schemes. In: Dunin-Kȩplicz, B., Jankowski, A., Skowron, A., Szczuka, M. (eds.) Monitoring, Security, and Rescue Techniques in Multiagent Systems. Advances in Soft Computing, pp. 191–202. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  173. Bazan, J.G.: Behavioral pattern identification through rough set modeling. Fundamenta Informaticae 72, 37–50 (2006)

    MATH  Google Scholar 

  174. Bazan, J.G., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J.: Risk pattern identification in the treatment of infants with respiratory failure through rough set modeling. In: Proceedings of the Eleventh Conference of Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2006), July 2-7, Paris, France, pp. 2650–2657 (2006)

    Google Scholar 

  175. Bazan, J.G., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J.: Rough set approach to behavioral pattern identification. Fundamenta Informaticae 75, 27–47 (2007)

    MathSciNet  MATH  Google Scholar 

  176. Bazan, J.G., Peters, J.F., Skowron, A.: Behavioral pattern identification through rough set modelling. In: Ślȩzak, D., et al. (eds.) RSFDGrC 2005. LNCS, vol. 3642, pp. 688–697. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  177. Bazan, J.G., Skowron, A.: On-line elimination of non-relevant parts of complex objects in behavioral pattern identification. In: Pal, S.K., et al. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 720–725. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  178. Bazan, J.G., Skowron, A.: Perception based rough set tools in behavioral pattern identification. In: Czaja, L. (ed.) Proceedings Workshop on Concurrency, Specification, and Programming (CS&P 2005), September 28-30, pp. 50–56. Warsaw University, Warsaw (2005)

    Google Scholar 

  179. Nguyen, S.H., Bazan, J., Skowron, A., Nguyen, H.S.: Layered learning for concept synthesis. LNCS Transactions on Rough Sets 3100, 187–208 (2004)

    MATH  Google Scholar 

  180. Bazan, J.G., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J.: Automatic planning based on rough set tools: Towards supporting treatment of infants with respiratory failure. In: Proceedings of the Workshop on Concurrency, Specification, and Programming (CS&P 2006), Wandlitz, Germany, September 27-29. Informatik-Bericht, vol. 170, pp. 388–399. Humboldt University, Berlin (2006)

    Google Scholar 

  181. Bazan, J.G., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J.: Automatic planning of treatment of infants with respiratory failure through rough set modeling. In: Greco, S., Hata, Y., Hirano, S., Inuiguchi, M., Miyamoto, S., Nguyen, H.S., Słowiński, R. (eds.) RSCTC 2006. LNCS, vol. 4259, pp. 418–427. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  182. Stone, P.: Layered Learning in Multi-Agent Systems: A Winning Approach to Robotic Soccer. The MIT Press, Cambridge (2000)

    Google Scholar 

  183. Bolc, L., Szałas, A. (eds.): Time and Logic: A Computational Approach. UCL Press, London (1995)

    MATH  Google Scholar 

  184. Clark, E., Emerson, E., Sistla, A.: Automatic verification of finite state concurrent systems using temporal logic specifications: A practical approach. ACM Transactions on Programming Languages and Systems 8, 244–263 (1986)

    Article  MATH  Google Scholar 

  185. Emerson, E.A.: Temporal and modal logic. In: van Leeuwen, J. (ed.) Handbook of Theoretical Computer Science: Volume B: Formal Models and Semantics, pp. 995–1072. Elsevier, Amsterdam (1990)

    Google Scholar 

  186. Skowron, A., Stepaniuk, J.: Constrained sums of information systems. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS, vol. 3066, pp. 300–309. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  187. Skowron, A., Stepaniuk, J.: Ontological framework for approximation. In: Ślȩzak, D., et al. (eds.) RSFDGrC 2005. LNCS, vol. 3641, pp. 718–727. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  188. Komorowski, J., Polkowski, L., Skowron, A.: Towards a rough mereology-based logic for approximate solution synthesis. Special Issue on Logics with Incomplete Information, Studia Logica 58, 143–184 (1997)

    MathSciNet  MATH  Google Scholar 

  189. Komorowski, J., Polkowski, L., Skowron, A.: Rough sets: a tutorial. In: Pal, S.K., Skowron, A. (eds.) Rough Fuzzy Hybridization: A New Trend in Decision-Making, pp. 3–98. Springer, Singapore (1999)

    Google Scholar 

  190. Polkowski, L., Skowron, A.: Rough mereology. In: Raś, Z.W., Zemankova, M. (eds.) ISMIS 1994. LNCS, vol. 869, pp. 85–94. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  191. Polkowski, L., Skowron, A.: Rough mereological approach to knowledge-based distributed ai. In: Lee, J.K., Liebowitz, J., Chae, Y.M. (eds.) Proceedings of the Third World Congress on Expert Systems, February 5-9, Soeul, Korea, pp. 774–781. Cognizant Communication Corporation (1996)

    Google Scholar 

  192. Polkowski, L., Skowron, A.: Rough mereology: A new paradigm for approximate reasoning. International Journal of Approximate Reasoning 15, 333–365 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  193. Polkowski, L., Skowron, A.: Rough mereology in information systems. a case study: Qualitative spatial reasoning. In: Polkowski, L., Lin, T.Y., Tsumoto, S. (eds.) Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. Studies in Fuzziness and Soft Computing, vol. 56. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  194. Polkowski, L., Skowron, A.: Rough mereological calculi of granules: A rough set approach to computation. Computational Intelligence 17, 472–492 (2001)

    Article  MathSciNet  Google Scholar 

  195. Bazan, J.G.: Dynamic reducts and statistical inference. In: Proceedings of the Sixth International Conference on Information Processing and Management of Uncertainty on Knowledge Based Systems (IPMU 1996), Granada, Spain, July 1-5, vol. III, pp. 1147–1152 (1996)

    Google Scholar 

  196. Bazan, J.G.: A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision tables. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methodology and Applications. Studies in Fuzziness and Soft Computing, vol. 18, pp. 321–365. Physica-Verlag, Heidelberg (1998)

    Google Scholar 

  197. Bazan, J.G.: Discovery of decision rules by matching new objects against data tables. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 521–528. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  198. Bazan, J.G.: Methods of approximate reasoning for synthesis of decision algorithms. PhD thesis, Warsaw University, Warsaw, Poland (1999) (in Polish)

    Google Scholar 

  199. Bazan, J.G., Nguyen, H.S., Nguyen, S.H., Synak, P., Wróblewski, J.: Rough set algorithms in classification problems. In: Polkowski, L., Lin, T.Y., Tsumoto, S. (eds.) Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. Studies in Fuzziness and Soft Computing, vol. 56, pp. 49–88. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  200. Bazan, J.G., Nguyen, H.S., Szczuka, M.: A view on rough set concept approximations. Fundamenta Informaticae 59, 107–118 (2004)

    MathSciNet  MATH  Google Scholar 

  201. Bazan, J.G., Skowron, A., Synak, P.: Discovery of decision rules from experimental data. In: Lin, T.Y. (ed.) Proceedings of the Third International Workshop on Rough Sets and Soft Computing (RSSC 1994), San Jose, CA, USA, pp. 526–533 (1994)

    Google Scholar 

  202. Bazan, J.G., Skowron, A., Synak, P.: Dynamic reducts as a tool for extracting laws from decision tables. In: Raś, Z.W., Zemankova, M. (eds.) ISMIS 1994. LNCS (LNAI), vol. 869, pp. 346–355. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  203. Bazan, J.G., Skowron, A., Synak, P.: Market data analysis: A rough set approach. ICS Research Reports 6, Warsaw University of Technology, Warsaw, Poland (1994)

    Google Scholar 

  204. Barwise, J., Seligman, J.: The logic of distributed systems. Cambridge University Press, Cambridge (1997)

    MATH  Google Scholar 

  205. Fahlman, S.E., Lebiere, C.: The Cascade-Correlation Learning Architecture. Advances in Neural Information Processing Systems, vol. II. Morgan Kaufmann, San Mateo (1990)

    Google Scholar 

  206. Cedrowska, J.: Prism: An algorithm for inducing modular rules. In: Gaines, B.R., Boose, J.H. (eds.) Knowledge Acquisition for Knowledge-based Systems. Academic Press, New York (1988)

    Google Scholar 

  207. Cestnik, B., Kononenko, I., Bratko, I.: Assistant 86: A knowledge elicitation tool for sophisticated users. In: Proceedings of EWSL 1987, Bled, Yugoslavia, pp. 31–47 (1997)

    Google Scholar 

  208. Goodman, R.M., Smyth, P.: The induction of probabilistic rule sets – the algorithm. In: Proceedings of the Sixth International Workshop on Machine Learning, San Mateo, CA, pp. 129–132. Morgan Kaufmann, San Francisco (1989)

    Chapter  Google Scholar 

  209. Quinlan, J.R.: The cascade-correlation learning architecture. In: Machine Learning 1, pp. 81–106. Kluwer Academic, Boston (1990)

    Google Scholar 

  210. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)

    Google Scholar 

  211. Utgoff, P.E.: Incremental learning of decision trees. In: Machine Learning 1, vol. IV, pp. 161–186. Kluwer Academic, Boston (1990)

    Google Scholar 

  212. Velde, W.V.D.: Idl, or taming the multiplexer. In: Proceedings of the Fourth European Working Session on Learning, Pitman, London, pp. 31–47 (1989)

    Google Scholar 

  213. Bloedorn, E., Michalski, R.S.: The multi-purpose incremental learning system AQ15 and its testing to three medical domains. In: Proceedings of the Third International Conference on Tools for AI, San Jose, CA (1991)

    Google Scholar 

  214. Clark, P., Niblett, T.: The CN2 induction algorithm. In: Machine Learning 3, pp. 261–284. Kluwer Academic, Boston (1989)

    Google Scholar 

  215. Grzymała-Busse, J.W.: LERS - a system for learning from examples based on rough sets. In: Słowiński, R. (ed.) Intelligent Decision Support - Handbook of Applications and Advances of the Rough Sets Theory. D: System Theory, Knowledge Engineering and Problem Solving, vol. 11, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  216. Grzymała-Busse, J.W.: LERS - a knowledge discovery system. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 2. Applications, Case Studies and Software Systems. Studies in Fuzziness and Soft Computing, pp. 562–565. Physica-Verlag, Heidelberg (1998)

    Google Scholar 

  217. Michalski, R.S., Mozetic, I., Hong, J., Lavrac, N.: The multi-purpose incremental learning system AQ15 and its testing to three medical domains. In: Proceedings of the AAAI 1986, San Mateo, CA, pp. 1041–1045. Morgan Kaufmann, San Francisco (1986)

    Google Scholar 

  218. Michalski, R.S., Wnȩk, J.: Constructive induction: An automated improvement of knowledge representation spaces for machine learning. In: Proceedings of a Workshop on Intelligent Information Systems, Practical Aspect of AI II, Augustów, Poland, pp. 188–236. Morgan Kaufmann, San Francisco (1993)

    Google Scholar 

  219. Mienko, R., Słowiński, R., Stefanowski, J., Susmaga, R.: Roughfamily - software implementation of rough set based data analysis and rule discovery techniques. In: Tsumoto, S. (ed.) Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets and Machine Discovery, Tokyo, Japan, November 6-8, pp. 437–440 (1996)

    Google Scholar 

  220. Øhrn, A., Komorowski, J.: ROSETTA - a rough set tool kit for analysis of data. In: Tsumoto, S. (ed.) Proceedings of the Fifth International Workshop on Rough Sets and Soft Computing (RSSC 1997) at the Third Joint Conference on Information Sciences (JCIS 1997), Research Triangle Park, NC, USA, March 2-5, pp. 403–407 (1997)

    Google Scholar 

  221. Słowiński, R., Stefanowski, J.: ’RoughDAS’ and ’RoughClass’ software implementations of the rough set approach. In: Słowiński, R. (ed.) Intelligent Decision Support - Handbook of Applications and Advances of the Rough Sets Theory. D: System Theory, Knowledge Engineering and Problem Solving, vol. 11. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  222. Stefanowski, J.: On rough set based approaches to induction of decision rules. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methodology and Applications. Studies in Fuzziness and Soft Computing, vol. 18, pp. 500–529. Physica-Verlag, Heidelberg (1998)

    Google Scholar 

  223. Stefanowski, J.: Algorithims of rule induction for knowledge discovery. Habilitation Thesis published as Series Rozprawy no. 361. Poznań University of Technology Press, Poznań (2001) (in Polish)

    Google Scholar 

  224. Dzeroski, S.: Handling noise in inductive logic programming. Master’s thesis, Dept. of EE and CS, University of Ljubljana, Ljubljana, Slovenia (1991)

    Google Scholar 

  225. Bazan, J.G., Latkowski, R., Szczuka, M.: DIXER - distributed executor for rough set exploration system. In: Ślȩzak, D., et al. (eds.) RSFDGrC 2005. LNCS, vol. 3642, pp. 39–47. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  226. Bazan, J.G., Szczuka, M.: RSES and RSESlib – a collection of tools for rough set computations. In: Ziarko, W., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 106–113. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  227. Bazan, J.G., Szczuka, M., Wojna, A., Wojnarski, M.: On the evolution of rough set exploration system. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS, vol. 3066, pp. 592–601. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  228. Bazan, J.G., Szczuka, M.S., Wróblewski, J.: A new version of rough set exploration system. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS, vol. 2475, pp. 397–404. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  229. Synak, P.: Rough Set Expert System, User’s Guide. Technical report, Warsaw University, Warsaw, Poland (1995)

    Google Scholar 

  230. Synak, P., Bazan, J.G., Cykier, A.: RSES Core Classes: The Technical Documentation. Technical report, Warsaw University, Warsaw, Poland (1996)

    Google Scholar 

  231. Szczuka, M.: Mikołajczyk, M., Bazan, J.G.: RSES 2.2 user’s guide. Technical report, Warsaw University, Warsaw, Poland (2005)

    Google Scholar 

  232. Polkowski, L.: Rough Sets: Mathematical Foundations. Advances in Soft Computing. Springer-Verlag/Physica-Verlag, Heidelberg (2002)

    Book  MATH  Google Scholar 

  233. Lipski, W.: On databases with incomplete information. Journal of the ACM 28, 41–70 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  234. Pawlak, Z.: Classification of objects by means of attributes. Polish Academy of Sciences 429 (1981)

    Google Scholar 

  235. Orłowska, E., Pawlak, Z.: Representation of nondeterministic information. Theoretical Computer Science 29, 27–39 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  236. Orłowska, E. (ed.): Incomplete Information: Rough Set Analysis. Studies in Fuzziness and Soft Computing, vol. 13. Physica-Verlag, Heidelberg (1998)

    MATH  Google Scholar 

  237. Nguyen, H.S.: Discretization of Real Value Attributes, Boolean Reasoning Approach. Ph.D thesis, Warsaw University, Warsaw, Poland (1997)

    Google Scholar 

  238. Nguyen, H.S.: Approximate boolean reasoning: Foundations and applications in data mining. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 334–506. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  239. Polkowski, L., Skowron, A.: Synthesis of decision systems from data tables. In: Lin, T.Y., Cecerone, N. (eds.) Rough Sets and Data Mining. Analysis for Imprecise Data. Advances in Soft Computing, pp. 259–299. Kluwer Academic, Dordrecht (1997)

    Google Scholar 

  240. Pawlak, Z., Skowron, A.: A rough set approach for decision rules generation. In: ICS Research Report 23/93, Warsaw University of Technology and Proceedings of the IJCAI 1993 Workshop W12: The Management of Uncertainty in AI, France (1993)

    Google Scholar 

  241. Tsumoto, S., Tanaka, H.: PRIMEROSE: Probabilistic rule induction method based on rough sets and resampling methods. Computational Intelligence 11, 389–405 (1995)

    Article  Google Scholar 

  242. Synak, P.: Methods of approximate reasoning in searching for rough dependencies. Master’s thesis, Warsaw University, Warsaw, Poland (1996) (in Polish)

    Google Scholar 

  243. Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  244. Bennett, B.: The role of definitions in construction and analysis of formal ontologies. In: Sixth Symposium on Logical Formalizations of Commonsense Reasoning, Palo Alto, CA, USA (2003)

    Google Scholar 

  245. Tarski, A.: Some methodological investigations on the definability of concepts. In: Logic, Semantics, Metamathematics. Clarendon Press, Oxford (1956)

    Google Scholar 

  246. Bazan, J.G., Skowron, A., Świniarski, R.W.: Rough sets and vague concept approximation: From sample approximation to adaptive learning. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 39–62. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  247. Skowron, A., Stepaniuk, J., Peters, J.F., Świniarski, R.W.: Calculi of approximation spaces. Fundamenta Informaticae 72, 363–378 (2006)

    MathSciNet  MATH  Google Scholar 

  248. Skowron, A., Świniarski, R.: Rough sets and higher order vagueness. In: Ślȩzak, D., et al. (eds.) RSFDGrC 2005. LNCS, vol. 3641, pp. 33–42. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  249. Skowron, A., Świniarski, R.W., Synak, P.: Approximation spaces and information granulation. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 175–189. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  250. Zadeh, L.: Fuzzy sets. Information and Control, 338–353 (1965)

    Google Scholar 

  251. Ajdukiewicz, K.: Pragmatic Logic. Reidel, Dordrecht (1974)

    Book  MATH  Google Scholar 

  252. Holland, J.H., Holyoak, K.J., Nisbett, R.E., Thagard, P.R.: Induction: processes of inference, learning, and discovery. MIT Press, Cambridge (1989)

    Google Scholar 

  253. Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27, 245–253 (1996)

    MathSciNet  MATH  Google Scholar 

  254. Pawlak, Z., Skowron, A.: Rough sets: Some extensions. Information Sciences 177, 28–40 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  255. Stepaniuk, J.: Knowledge discovery by application of rough set models. In: Polkowski, L., Lin, T.Y., Tsumoto, S. (eds.) FCT 1977. Studies in Fuzziness and Soft Computing, vol. 56, pp. 137–233. Springer-Verlag/Physica-Verlag, Heidelberg (2000)

    Chapter  Google Scholar 

  256. Ziarko, W.: Variable precision rough set model. Journal of Computer and System Sciences 46, 39–59 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  257. Provost, F., Kohavi, R.: On applied research in machine learning. Machine Learning 30, 127–132 (1998)

    Article  Google Scholar 

  258. Weiss, S.M., Kulikowski, C.A.: Computer Systems That Learn. Morgan Kaufmann, San Mateo (1991)

    Google Scholar 

  259. ROSETTA: Project web site, http://rosetta.lcb.uu.se/general

  260. Altman, D.G.: Practical Statistics for Medical Research. Chapman and Hall/CRC, London (1997)

    Google Scholar 

  261. Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27, 861–874 (2006)

    Article  Google Scholar 

  262. Swets, J.A.: Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  263. Øhrn, A., Komorowski, J., Skowron, A., Synak, P.: The design and implementation of a knowledge discovery toolkit based on rough sets: The ROSETTA system. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methodology and Applications. Studies in Fuzziness and Soft Computing, vol. 18, pp. 376–399. Physica-Verlag, Heidelberg (1998)

    Google Scholar 

  264. Øhrn, A., Komorowski, J., Skowron, A., Synak, P.: The ROSETTA software system. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 2. Applications, Case Studies and Software Systems. Studies in Fuzziness and Soft Computing, pp. 572–576. Physica-Verlag, Heidelberg (1998)

    Google Scholar 

  265. Efron, B.: Estimating the error rate of a prediction rule: improvement on cross validation. Journal of American Statistics Association 78, 316–331 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  266. Stefanowski, J.: Classification and decision supporting based on rough set theory. Foundations of Computing and Decision Sciences 18, 371–380 (1993)

    MathSciNet  MATH  Google Scholar 

  267. Delimata, P., Moshkov, M., Skowron, A., Suraj, Z.: Two families of classification algorithms. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds.) RSFDGrC 2007. LNCS, vol. 4482, pp. 297–304. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  268. Bazan, J.G., Nguyen, H.S., Skowron, A., Szczuka, M.S.: A view on rough set concept approximations. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS, vol. 2639, pp. 181–188. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  269. Skowron, A., Peters, J.F.: Rough sets: Trends and challenges - plenary paper. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS, vol. 2639, pp. 25–34. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  270. Webster: Webster’s New Collegiate Dictionary. Merriam-Webster, Springfield (1991)

    Google Scholar 

  271. Liliana, A.: Material and formal ontology. In: Roberto, P., Peter, S. (eds.) Formal ontology. Advanced in Soft Computing, pp. 199–232. Kluwer, Dordrecht (1996)

    Google Scholar 

  272. Guarino, N., Poli, R.: Formal ontology in conceptual analysis and knowledge representation. Kluwer, Dordrecht (1993)

    Google Scholar 

  273. Shapiro, S.C.: Encyclopedia of Artificial Intelligence. John Wiley and Sons, New York (1992)

    Google Scholar 

  274. Booch, G.: Object-oriented Analysis and Design with Applications. Addison-Wesley Publishing Company, Santa Clara (1994)

    MATH  Google Scholar 

  275. Taylor, D.A.: Object-Oriented Information Systems: Planning and Implementation. John Wiley & Sons, New York (1992)

    Google Scholar 

  276. Jones, D., Bench-Capon, T., Visser, P.: Ontology-based support for human disease study. In: Proceedings of the IT&KNOWS Conference, XV IFIP World Computer Congress (August 1998)

    Google Scholar 

  277. Uschold, M., Grüninger, M.: Ontologies: principles, methods, and applications. Knowledge Engineering Review 11, 93–155 (1996)

    Article  Google Scholar 

  278. Uschold, M.: Building ontologies: Towards a unified methodology. In: Proceedings 16th Annual Conference of the British Computer Society Specialist Group on Expert Systems, Cambridge, UK (1996)

    Google Scholar 

  279. Dublin Core: Project web site, http://dublincore.org/

  280. General Formal Ontology (GFO): Project web site, http://www.onto-med.de/

  281. OpenCyc/ResearchCyc: Project web site, http://research.cyc.com/

  282. Suggested Upper Merged Ontology (SUMO): Project web site, http://www.articulatesoftware.com/

  283. WordNet: Project web site, http://wordnet.princeton.edu/

  284. Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE): Project web site, http://www.loa-cnr.it/DOLCE.html

  285. W3C: RDF Primer, W3C Recommendation. Technical report, The World Wide Web Consortium Technical Report (2004), http://www.w3.org/RDF/

  286. OIL - Ontology Inference Layer: Project web site, www.ontoknowledge.org/oil/

  287. DAML – DARPA Agent Markup Language: Project web site, www.daml.org

  288. Cyc: Project web site, http://www.cyc.com

  289. OpenCyc: Project web site, http://opencyc.org

  290. Protege: Project web site, http://protege.stanford.edu

  291. OntoStudio: Project web site, http://www.ontoprise.de

  292. Ontolingua: Project web site, www.ksl.stanford.edu/software/ontolingua/

  293. Chimaera: Project web site, http://ksl.stanford.edu/software/chimaera/

  294. OilEd: Project web site, http://oiled.man.ac.uk/

  295. W3C: RDQL - a query language for RDF, W3C member submission. Technical report, The World Wide Web Consortium Technical Report (2004), http://www.w3.org/Submission/RDQL

  296. Fahle, M., Poggio, T.: Perceptual Learning. MIT Press, Cambridge (2002)

    Google Scholar 

  297. Harnad, S.: Categorical Perception: The Groundwork of Cognition. Cambridge University Press, New York (1987)

    Google Scholar 

  298. McCarthy, J.: Notes on formalizing context. In: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI 1993). Morgan Kaufmann, Chambéry (1993)

    Google Scholar 

  299. McCarthy, J., Hayes, P.: Some philosophical problems from the standpoint of artificial intelligence. In: Meltzer, B., Michie, D. (eds.) Machine Intelligence, vol. IV, pp. 463–502. Edinburgh University Press, Edinburgh (1969)

    Google Scholar 

  300. Anderson, J.R.: Rules of the mind. Lawrence Erlbaum, Hillsdale (1993)

    Google Scholar 

  301. Kieras, D., Meyer, D.E.: An overview of the epic architecture for cognition and performance with application to human-computer interaction. Human-Computer Interaction 12, 391–438 (1997)

    Article  Google Scholar 

  302. Laird, J., Newell, A., Rosenbloom, P.: Soar: An architecture for general intelligence. Artificial Intelligence 33, 1–64 (1987)

    Article  Google Scholar 

  303. Veloso, M.M., Carbonell, J.G.: Derivational analogy in prodigy: Automating case acquisition, storage, and utilization. Machine Learning 10, 249–278 (1993)

    Article  Google Scholar 

  304. Tarski, A.: The semantic concept of truth. Philosophy and Phenomenological Research 4, 341–375 (1944)

    Article  Google Scholar 

  305. Dictionary, T.F.: Project web site, http://www.thefreedictionary.com

  306. Bazan, J.G., Nguyen, S.H., Nguyen, H.S., Skowron, A.: Rough set methods in approximation of hierarchical concepts. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS, vol. 3066, pp. 346–355. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  307. Nguyen, S.H., Nguyen, H.S.: Improving Rough Classifiers Using Concept Ontology. In: Ho, T.B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS, vol. 3518, pp. 312–322. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  308. Greco, S., Matarazzo, B., Słowiński, R.: A new rough set approach to multicriteria and multiattribute classification. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 60–67. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  309. Greco, S., Matarazzo, B., Słowiński, R.: Rough approximation of preference relation by dominance relations. ICS Research Report 16/1996, Warsaw University of Technology, Warsaw, Poland; also in Journal of Operational Research 117, 63–83 (1999)

    Google Scholar 

  310. Greco, S., Matarazzo, B., Słowiński, R.: Multicriteria classification. In: Kloesgen, W., Zytkow, J. (eds.) Handbook of Data Mining and Knowledge Discovery, pp. 318–328. Oxford University Press, Inc., New York (2002)

    Google Scholar 

  311. Greco, S., Matarazzo, B., Słowiński, R.: Rough approximation by dominance relations. International Journal of Intelligent Systems 17, 153–171 (2002)

    Article  MATH  Google Scholar 

  312. Błaszczyński, J., Greco, S., Słowiński, R.: Multi-criteria classification – a new scheme for application of dominance-based decision rules. Journal of Operational Research 181, 1030–1044 (2007)

    Article  MATH  Google Scholar 

  313. Błaszczyński, J., Słowiński, R.: Incremental induction of decision rules from dominance-based rough approximations. In: Skowron, A., Szczuka, M. (eds.) Electronic Notes in Theoretical Computer Science, vol. 82. Springer, Heidelberg (2003)

    Google Scholar 

  314. Błaszczyński, J., Słowiński, R.: Incremental induction of satisfactory decision rules from dominance based rough approximations. In: Skowron, A., Szczuka, M. (eds.) Proceedings of the International Workshop on Rough Sets in Knowledge Discovery and Soft Computing (RSKD 2003), Warsaw, Poland, April 12-13, pp. 40–51 (2003)

    Google Scholar 

  315. Greco, S., Matarazzo, B., Słowiński, R., Stefanowski, J.: An algorithm for induction of decision rules consistent with the dominance principle. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS, vol. 2005, pp. 304–313. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  316. Peters, J.F.: Time and clock information systems: Concepts and rough fuzzy petri net models. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 2. Applications, Case Studies and Software Systems. Studies in Fuzziness and Soft Computing, pp. 385–417. Springer-Verlag, Berlin (1998)

    Chapter  Google Scholar 

  317. Polkowski, L.: Granulation of knowledge in decision systems: The approach based on rough inclusions. The method and its applications. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds.) RSEISP 2007. LNCS, vol. 4585, pp. 69–79. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  318. Synak, P.: Temporal Aspects of Data Analysis: A Rough Set Approach. Ph.D thesis, The Institute of Computer Science of the Polish Academy of Sciences, Warsaw, Poland (2003) (in Polish) (defended in 2004)

    Google Scholar 

  319. Polkowski, L., Artiemjew, P.: On granular rough computing with missing values. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds.) RSEISP 2007. LNCS, vol. 4585, pp. 271–279. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  320. Skowron, A.: Toward intelligent systems: Calculi of information granules. In: Terano, T., Nishida, T., Namatame, A., Tsumoto, S., Ohsawa, Y., Washio, T. (eds.) JSAI-WS 2001. LNCS (LNAI), vol. 2253, pp. 251–260. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  321. Stepaniuk, J.: Approximation spaces, reducts and representatives. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methodology and Applications. Studies in Fuzziness and Soft Computing, vol. 18, pp. 109–126. Physica-Verlag, Heidelberg (1998)

    Chapter  Google Scholar 

  322. Stepaniuk, J.: Knowledge discovery by application of rough set models. In: Polkowski, L., Lin, T.Y., Tsumoto, S. (eds.) Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. Studies in Fuzziness and Soft Computing, vol. 56, pp. 137–233. Physica-Verlag, Heidelberg (2000)

    Chapter  Google Scholar 

  323. Yao, Y.Y.: Perspectives of granular computing. In: Proceedings of the Conference on Granular Computing (GrC 2005), Beijing, China, New York. IEEE Press, Los Alamitos (2005)

    Google Scholar 

  324. Bazan, J.G., Osmólski, A., Skowron, A., Ślȩzak, D., Szczuka, M., Wróblewski, J.: Rough set approach to survival analysis. In: Suraj, Z. (ed.) Proceedings of the Sixth International Conference on Soft Computing and Distributed Processing (SCDP 2002), June 24-25, pp. 45–48. University of Information Technology and Management in Rzeszów Press, Rzeszów (2002)

    Google Scholar 

  325. Bazan, J.G., Osmólski, A., Skowron, A., Ślȩzak, D., Szczuka, M., Wróblewski, J.: Rough set approach to the survival analysis. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS, vol. 2475, pp. 522–529. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  326. Bazan, J.G., Skowron, A., Ślȩzak, D., Wróblewski, J.: Searching for the complex decision reducts: The case study of the survival analysis. In: Raś, Z.W., Zhong, N., Tsumoto, S., Suzuku, E. (eds.) ISMIS 2003. LNCS, vol. 2871, pp. 160–168. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  327. Vincent, J.L., de Mendonca, A., Cantraine, F., et al.: Use of the sofa score to assess the incidence of organ dysfunction. Crit Care Medicine 26, 1793–1800 (1998)

    Article  Google Scholar 

  328. Hurford, W.E., Bigatello, L.M., Haspel, K.L., Hess, D.R., Warren, R.L.: Critical care handbook of the Massachusetts General Hospital, 3rd edn. Lippincott Williams & Wilkins, Philadelphia (2000)

    Google Scholar 

  329. Revelation Software: Web site, http://www.revelation.com/

  330. Ginsberg, M.L.: Approximate planning. Artificial Intelligence 76, 89–123 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  331. Ginsberg, M.L.: A new algorithm for generative planning. In: Aiello, L.C., Doyle, J., Shapiro, S. (eds.) KR 1996: Principles of Knowledge Representation and Reasoning, pp. 186–197. Morgan Kaufmann, San Francisco (1996)

    Google Scholar 

  332. Fikes, R., Nilsson, N.: STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2, 189–208 (1971)

    Article  MATH  Google Scholar 

  333. Blum, A., Furst, M.: Fast planning through planning graph analysis. Artificial Intelligence 90, 281–300 (1997)

    Article  MATH  Google Scholar 

  334. Ernst, M., Millstein, T.D., Weld, D.S.: Automatic SAT-compilation of planning problems. In: Proceedings of the IJCAI 1997, pp. 1169–1177 (1997)

    Google Scholar 

  335. Kautz, H., Selman, B.: Unifying SAT-based and graph-based planning. In: Minker, J. (ed.) Proceedings of the Workshop on Logic-Based Artificial Intelligence, Washington, DC, College Park, Maryland, Computer Science Department, University of Maryland, June 14–16 (1999)

    Google Scholar 

  336. Kautz, H.A., McAllester, D., Selman, B.: Encoding plans in propositional logic. In: Proceedings of the Fifth International Conference on the Principle of Knowledge Representation and Reasoning (KR 1996), pp. 374–384 (1996)

    Google Scholar 

  337. Kautz, H.A., Selman, B.: Planning as satisfiability. In: Proceedings of the Tenth European Conference on Artificial Intelligence (ECAI 1992), pp. 359–363 (1992)

    Google Scholar 

  338. Gerevini, A., Serina, I.: LPG: a planner based on planning graphs with action costs. In: Proceedings of the Sixth International Conference on AI Planning and Scheduling, pp. 13–22. AAAI Press, Menlo Park (2002)

    Google Scholar 

  339. Bylander, T.: A linear programming heuristic for optimal planning. In: Proceedings of the 14th National Conference on Artificial Intelligence (AAAI 1997), Providence, Rhode Island, pp. 694–699. AAAI Press/MIT Press, Menlo Park (1997)

    Google Scholar 

  340. Gałuszka, A., Świerniak, A.: Translation STRIPS planning in multi-robot environment to linear programming. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS, vol. 3070, pp. 768–773. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  341. van Beek, P., Chen, X.: Cplan: A constraint programming approach to planning. In: Proceedings of the 16th National Conference on Artificial Intelligence (IJCAI 1999), pp. 585–590 (1999)

    Google Scholar 

  342. Veloso, M.: Planning and Learning by Analogical Reasoning. Springer, Heidelberg (1994)

    Book  MATH  Google Scholar 

  343. Vere, S.: Planning in time: Windows and durations for activities and goals. IEEE Transactions on Pattern Analysis and Machine Intelligence 5, 246–267 (1983)

    Article  Google Scholar 

  344. Sacerdoti, E.: The nonlinear nature of plans. In: Proceedings of the Fourth International Joint Conference on Artificial Intelligence (IJCAI 1975), pp. 206–214 (1975)

    Google Scholar 

  345. Tate, A.: Generating project networks. In: Proceedings of the Fifth International Joint Conference on Artificial Intelligence (IJCAI 1977), pp. 888–893 (1977)

    Google Scholar 

  346. TLPlan: Project web site, http://www.cs.toronto.edu/~fbacchus/tlplan.html

  347. Doherty, P., Kvarnstrom, J.: TALplanner: A temporal logic-based planner. AI Magazine 22, 95–102 (2001)

    MATH  Google Scholar 

  348. TALplanner: Project web site, http://www.ida.liu.se/~patdo/aiicssite1/kplab/projects/talplanner/

  349. Anzai, Y.: Pattern recognition and machine learning. Academic Press, San Diego (1992)

    MATH  Google Scholar 

  350. Bernardinello, L., Cindio, F.D.: A survey of basic net models and modular net classes. In: Rozenberg, G. (ed.) APN 1992. LNCS, vol. 609. Springer, Heidelberg (1992)

    Google Scholar 

  351. Suyama, T., Yokoo, M.: Strategy/false-name proof protocols for combinatorial multi-attribute procurement auction. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2004), New York, NY, USA, August 19-23, 2004, pp. 160–167. IEEE Computer Society, Los Alamitos (2005)

    Google Scholar 

  352. Yokoo, M.: Protocol/mechanism design for cooperation/competition. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2004), New York, NY, USA, August 19-23, pp. 3–7. IEEE Computer Society, Los Alamitos (2005)

    Google Scholar 

  353. Góra, G., Bazan, J.G., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J.: Case-based planning of treatment of infants with respiratory failure. In: Czaja, L. (ed.) Proceedings Workshop on Concurrency, Specification, and Programming (CS&P 2007), Łagów, Poland, Warsaw, Warsaw University, September 27-28, pp. 223–234 (2007)

    Google Scholar 

  354. Góra, G., Bazan, J.G., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J.: Case-based planning of treatment of infants with respiratory failure. Fundamenta Informaticae (to appear, 2008)

    Google Scholar 

  355. Simulator, R.: Project web site, http://logic.mimuw.edu.pl/~bazan/simulator

Download references

Author information

Authors and Affiliations

  1. Chair of Computer Science, University of Rzeszów, Rejtana 16A, 35-310, Rzeszów, Poland

    Jan G. Bazan

Authors
  1. Jan G. Bazan
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  3. Institute of Computer Science, Warsaw University of Technology, Nowowoiejska 15/19, 00-665, Warsaw, Poland

    Henryk Rybiński

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Bazan, J.G. (2008). Hierarchical Classifiers for Complex Spatio-temporal Concepts. In: Peters, J.F., Skowron, A., Rybiński, H. (eds) Transactions on Rough Sets IX. Lecture Notes in Computer Science, vol 5390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89876-4_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-89876-4_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89875-7

  • Online ISBN: 978-3-540-89876-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation