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.
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References
Baldi, P., Brunak, S.: Bioinformatics: The Machine Learning Approach. MIT Press, Cambridge (1998)
Banerjee, M., Mitra, S., Pal, S.K.: Rough Fuzzy MLP: Knowledge Encoding and Classification. IEEE Trans. Neural Networks 9, 1203–1216 (1998)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithm. Plenum, New York (1981)
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)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2005)
http://www.daviddlewis.com/resources/testcollections/reuters21578/
Kolodner, J.L.: Case-Based Reasoning. Morgan Kaufmann, San Mateo (1993)
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)
Kuipers, B.J.: Qualitative Reasoning. MIT Press, Cambridge (1984)
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)
Lingras, P., West, C.: Interval set clustering of web users with rough K-means. Journal of Intelligent Information Systems 23, 5–16 (2004)
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)
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)
Maji, P., Pal, S.K.: Feature Selection Using f-Information Measures in Fuzzy Approximation Spaces. IEEE Trans. Knowledge and Data Engineering (to appear)
Mitra, S., Banka, H., Pedrycz, W.: Rough-fuzzy collaborative clustering. IEEE Trans. on Systems, Man, and Cybernetics - Part B: Cybernetics 36, 795–805 (2006)
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)
National Center for Biotechnology Information, http://www.ncbi.nlm.nih.gov
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)
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)
Pal, S.K., Skowron, A. (eds.): Rough-Fuzzy Hybridization: A New Trend in Decision Making. Springer, Singapore (1999)
Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-neuro Computing: A Way to Computing with Words. Springer, Berlin (2003)
Pal, S.K., Skowron, A. (eds.): Special issue on Rough Sets, Pattern Recognition and Data Mining. Pattern Recognition Letters 24 (2003)
Pal, S.K., Dillon, T.S., Yeung, D.S. (eds.): Soft Computing in Case Based Reasoning. Springer, London (2001)
Pal, S.K., Shiu, S.C.K.: Foundations of Soft Case Based Reasoning. John Wiley, NY (2003)
Pal, S.K., Mitra, P.: Case generation using rough sets with fuzzy discretization. IEEE Trans. Knowledge and Data Engineering 16, 292–300 (2004)
Pal, S.K., Mitra, P.: Pattern Recognition Algorithms for Data Mining. Chapman & Hall CRC Press, Boca Raton (2004)
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)
Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing Techniques for Computing with Words. Springer, Berlin (2004)
Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.): PReMI 2005. LNCS, vol. 3776. Springer, Heidelberg (2005)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic, Dordrecht (1991)
Pedrycz, W., Skowron, A., Kreinovich, V. (eds.): Handbook of Granular Computing. John Wiley, N.Y. (2008)
Ray, S.S., Bandyopadhyay, S., Mitra, P., Pal, S.K.: Bioinformatics in Neurocomputing Framework. IEE Proc. Circuits Devices & Systems 152, 556–564 (2005)
Saha, S., Murthy, C.A., Pal, S.K.: Rough set Based Ensemble Classifier for Web Page Classification. Fundamentae Informetica 76, 171–187 (2007)
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)
Sen, D., Pal, S.K.: Histogram Thresholding using Fuzzy and Rough Measures of Association Error. IEEE Trans. Image Processing 18, 879–888 (2009)
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)
Sun, R.: Integrating Rules and Connectionism for Robust Commonsense Reasoning. Wiley, N.Y. (1994)
Swiniarski, R.W., Skowron, A.: Rough Set Methods in Feature Selection and Recognition. Pattern Recognition Letters 24, 833–849 (2003)
Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.): RSKT 2008. LNCS (LNAI), vol. 5009. Springer, Heidelberg (2008)
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)
Zadeh, L.A.: A new direction in AI: Toward a computational theory of perceptions. AI Magazine 22, 73–84 (2001)
Zadeh, L.A., Pal, S.K., Mitra, S.: Foreword. In: Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing. Wiley, New York (1999)
Zadeh, L.A.: Fuzzy Logic, Neural Networks, and Soft Computing. ACM 37, 77–84 (1994)
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)
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Pal, S.K. (2010). Computational Theory Perception (CTP), Rough-Fuzzy Uncertainty Analysis and Mining in Bioinformatics and Web Intelligence: A Unified Framework. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets XI. Lecture Notes in Computer Science, vol 5946. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11479-3_7
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