Transactions on Rough Sets XI pp 106-129

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5946)

Computational Theory Perception (CTP), Rough-Fuzzy Uncertainty Analysis and Mining in Bioinformatics and Web Intelligence: A Unified Framework

  • Sankar K. Pal

Abstract

The concept of computational theory of perceptions (CTP), its characteristics and the relation with fuzzy-granulation (f-granulation) are explained. Role of f-granulation in machine and human intelligence and its modeling through rough-fuzzy integration are discussed. The Significance of rough-fuzzy synergestic integration is highlighted through three examples, namely, rough-fuzzy case generation, rough-fuzzy c-means and rough-fuzzy c-medoids along with the role of fuzzy granular computation. Their superiority, in terms of performance and computation time, is illustrated for the tasks of case generation (mining) in large-scale case-based reasoning systems, segmenting brain MR images, and analyzing protein sequences. Different quantitative measures for rough-fuzzy clustering are explained. The effectiveness of rough sets in constructing an ensemble classifier is also illustrated in a part of the article along with its performance for web service classification. The article includes some of the existing results published elsewhere under different topics related to rough sets and attempts to integrate them with CTP in a unified framework providing a new direction of research.

Keywords

soft computing fuzzy granulation rough-fuzzy computing bioinformatics MR image segmentation case based reasoning data mining web service classification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baldi, P., Brunak, S.: Bioinformatics: The Machine Learning Approach. MIT Press, Cambridge (1998)Google Scholar
  2. 2.
    Banerjee, M., Mitra, S., Pal, S.K.: Rough Fuzzy MLP: Knowledge Encoding and Classification. IEEE Trans. Neural Networks 9, 1203–1216 (1998)CrossRefGoogle Scholar
  3. 3.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithm. Plenum, New York (1981)Google Scholar
  4. 4.
    Dayhoff, M.O., Schwartz, R.M., Orcutt, B.C.: A Model of Evolutionary Change in Proteins. Matrices for Detecting Distant Relationships, Atlas of Protein Sequence and Structure 5, 345–358 (1978)Google Scholar
  5. 5.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2005)Google Scholar
  6. 6.
  7. 7.
  8. 8.
  9. 9.
  10. 10.
    Kolodner, J.L.: Case-Based Reasoning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  11. 11.
    Krishnapuram, R., Joshi, A., Nasraoui, O., Yi, L.: Low complexity fuzzy relational clustering algorithms for web mining. IEEE Trans. on Fuzzy System 9, 595–607 (2001)CrossRefGoogle Scholar
  12. 12.
    Kuipers, B.J.: Qualitative Reasoning. MIT Press, Cambridge (1984)Google Scholar
  13. 13.
    Li, Y., Shiu, S.C.K., Pal, S.K.: Combining feature reduction and case selection in building CBR classifiers. IEEE Trans. on Knowledge and Data Engineering 18, 415–429 (2006)CrossRefGoogle Scholar
  14. 14.
    Lingras, P., West, C.: Interval set clustering of web users with rough K-means. Journal of Intelligent Information Systems 23, 5–16 (2004)MATHCrossRefGoogle Scholar
  15. 15.
    Maji, P., Pal, S.K.: Rough set based generalized fuzzy C-means algorithm and quantitative indices. IEEE Trans. on System, Man and Cybernetics, Part B, 37, 1529–1540 (2007)CrossRefGoogle Scholar
  16. 16.
    Maji, P., Pal, S.K.: Rough-fuzzy C-medoids algorithm and selection of bio-basis for amino acid sequence analysis. IEEE Trans. Knowledge and Data Engineering 19, 859–872 (2007)CrossRefGoogle Scholar
  17. 17.
    Maji, P., Pal, S.K.: Feature Selection Using f-Information Measures in Fuzzy Approximation Spaces. IEEE Trans. Knowledge and Data Engineering (to appear)Google Scholar
  18. 18.
    Mitra, S., Banka, H., Pedrycz, W.: Rough-fuzzy collaborative clustering. IEEE Trans. on Systems, Man, and Cybernetics - Part B: Cybernetics 36, 795–805 (2006)CrossRefGoogle Scholar
  19. 19.
    Mitra, S., De, R.K., Pal, S.K.: Knowledge Based Fuzzy MLP for Classification and Rule Generation. IEEE Trans. Neural Networks 8, 1338–1350 (1997)CrossRefGoogle Scholar
  20. 20.
    National Center for Biotechnology Information, http://www.ncbi.nlm.nih.gov
  21. 21.
    Pal, S.K., Talwar, V., Mitra, P.: Web mining in soft computing framework: Relevance, state of the art and future directions. IEEE Trans. Neural Networks 13, 1163–1177 (2002)CrossRefGoogle Scholar
  22. 22.
    Pal, S.K., Bandyopadhyay, S., Ray, S.S.: Evolutionary Computation in Bioinformatics: A Review. IEEE Transactions on Systems, Man, and Cybernetics, Part-C 36, 601–615 (2006)CrossRefGoogle Scholar
  23. 23.
    Pal, S.K., Skowron, A. (eds.): Rough-Fuzzy Hybridization: A New Trend in Decision Making. Springer, Singapore (1999)MATHGoogle Scholar
  24. 24.
    Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-neuro Computing: A Way to Computing with Words. Springer, Berlin (2003)Google Scholar
  25. 25.
    Pal, S.K., Skowron, A. (eds.): Special issue on Rough Sets, Pattern Recognition and Data Mining. Pattern Recognition Letters 24 (2003)Google Scholar
  26. 26.
    Pal, S.K., Dillon, T.S., Yeung, D.S. (eds.): Soft Computing in Case Based Reasoning. Springer, London (2001)MATHGoogle Scholar
  27. 27.
    Pal, S.K., Shiu, S.C.K.: Foundations of Soft Case Based Reasoning. John Wiley, NY (2003)Google Scholar
  28. 28.
    Pal, S.K., Mitra, P.: Case generation using rough sets with fuzzy discretization. IEEE Trans. Knowledge and Data Engineering 16, 292–300 (2004)CrossRefGoogle Scholar
  29. 29.
    Pal, S.K., Mitra, P.: Pattern Recognition Algorithms for Data Mining. Chapman & Hall CRC Press, Boca Raton (2004)MATHGoogle Scholar
  30. 30.
    Pal, S.K., Ghosh, A., Sankar, B.U.: Segmentation of remotely sensed images with fuzzy thresholding, and quantitative evaluation. International Journal of Remote Sensing 2, 2269–2300 (2000)Google Scholar
  31. 31.
    Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing Techniques for Computing with Words. Springer, Berlin (2004)MATHGoogle Scholar
  32. 32.
    Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.): PReMI 2005. LNCS, vol. 3776. Springer, Heidelberg (2005)Google Scholar
  33. 33.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic, Dordrecht (1991)MATHGoogle Scholar
  34. 34.
    Pedrycz, W., Skowron, A., Kreinovich, V. (eds.): Handbook of Granular Computing. John Wiley, N.Y. (2008)Google Scholar
  35. 35.
    Ray, S.S., Bandyopadhyay, S., Mitra, P., Pal, S.K.: Bioinformatics in Neurocomputing Framework. IEE Proc. Circuits Devices & Systems 152, 556–564 (2005)CrossRefGoogle Scholar
  36. 36.
    Saha, S., Murthy, C.A., Pal, S.K.: Rough set Based Ensemble Classifier for Web Page Classification. Fundamentae Informetica 76, 171–187 (2007)MathSciNetGoogle Scholar
  37. 37.
    Saha, S., Murthy, C.A., Pal, S.K.: Classification of Web Services using Tensor Space Model and Rough Ensemble Classifier. In: Proc. 17th International Symposium on Methodologies for Intelligent Systems, Toronto, Canada, pp. 508–513 (2008)Google Scholar
  38. 38.
    Sen, D., Pal, S.K.: Histogram Thresholding using Fuzzy and Rough Measures of Association Error. IEEE Trans. Image Processing 18, 879–888 (2009)CrossRefGoogle Scholar
  39. 39.
    Sen, D., Pal, S.K.: Generalized Rough Sets, Entropy and Image Ambiguity Measures. IEEE Trans. Syst, Man and Cyberns. Part B 39, 117–128 (2009)CrossRefGoogle Scholar
  40. 40.
    Sun, R.: Integrating Rules and Connectionism for Robust Commonsense Reasoning. Wiley, N.Y. (1994)MATHGoogle Scholar
  41. 41.
    Swiniarski, R.W., Skowron, A.: Rough Set Methods in Feature Selection and Recognition. Pattern Recognition Letters 24, 833–849 (2003)MATHCrossRefGoogle Scholar
  42. 42.
    Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.): RSKT 2008. LNCS (LNAI), vol. 5009. Springer, Heidelberg (2008)MATHGoogle Scholar
  43. 43.
    Yao, Y.Y.: Granular Computing: Basic Issues and Possible Solutions. In: Proceedings of the 5th Joint Conference on Information Sciences, vol. I, pp. 186–189 (2000)Google Scholar
  44. 44.
    Zadeh, L.A.: A new direction in AI: Toward a computational theory of perceptions. AI Magazine 22, 73–84 (2001)Google Scholar
  45. 45.
    Zadeh, L.A., Pal, S.K., Mitra, S.: Foreword. In: Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing. Wiley, New York (1999)Google Scholar
  46. 46.
    Zadeh, L.A.: Fuzzy Logic, Neural Networks, and Soft Computing. ACM 37, 77–84 (1994)CrossRefGoogle Scholar
  47. 47.
    Zadeh, L.A.: Towards a Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets Systems 19, 111–127 (1997)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sankar K. Pal
    • 1
  1. 1.Center for Soft Computing Research: A National FacilityIndian Statistical InstituteKolkata

Personalised recommendations