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Multi-granularity Intelligent Information Processing

  • Guoyin WangEmail author
  • Ji Xu
  • Qinghua Zhang
  • Yuchao Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9437)

Abstract

Multi-granularity thinking, computation and problem solving are effective approaches for human being to deal with complex and difficult problems. Deep learning, as a successful example model of multi-granularity computation, has made significant progress in the fields of face recognition, image automatic labeling, speech recognition, and so on. Its idea can be generalized as a model of solving problems by joint computing on multi-granular information/knowledge representation (MGrIKR) in the perspective of granular computing (GrC). This paper introduces our research on constructing MGrIKR from original datasets and its application in big data processing. Firstly, we have a survey about the study of the multi-granular computing (MGrC), including the four major theoretical models (rough sets, fuzzy sets, quotient space,and cloud model) for MGrC. Then we introduce the five representative methods for constructing MGrIKR based on rough sets, computing with words(CW), fuzzy quotient space based on information entropy, adaptive Gaussian cloud transformation (A-GCT), and multi-granularity clustering based on density peaks, respectively. At last we present an MGrC based big data processing framework, in which MGrIKR is built and taken as the input of other machine learning and data mining algorithms.

Keywords

Multi-granularity Fuzzy sets Rough sets Quotient space Cloud model Hierarchical clustering Density peaks Deep learning 

Notes

Acknowledgement

This work is partly supported by the National Natural Science Foundation of China under Grant numbers of 61272060, 61472056 and 61305055, and Natural Science Foundation Key Project of Chongqing of P. R. China under Grant No. CSTC2013jjB40003.

References

  1. 1.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)CrossRefGoogle Scholar
  2. 2.
    Taigman, Y., Yang, M., Ranzato, M.A., et al.: Deepface: Closing the gap to human-level performance in face verification. In: IEEE Conference on Computer Vision & Pattern Recognition, CVPR 2014, pp. 1701–1708 (2014)Google Scholar
  3. 3.
    Vinyals, O., Toshev, A., Engio, S., Erhan, D.: Show and Tell: A Neural Image Caption Generator. In: IEEE Conference on Computer Vision & Pattern Recognition, CVPR 2015, pp. 3156–3164 (2015)Google Scholar
  4. 4.
    Graves, A., Mohamed, A., Hinton, G.: Speech recognition with deep recurrent neural networks. In: IEEE International Conference on Acoustics, Speech & Signal Processing, ICASSP 2013, pp. 6645–6649 (2013)Google Scholar
  5. 5.
    Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans SMC. 23, 665–685 (1993)Google Scholar
  6. 6.
    Wang, G., Shi, H.: TMLNN: triple-valued or multiple-valued logic neural network. IEEE Trans. Neural Netw. 9, 1099–1117 (1998)CrossRefGoogle Scholar
  7. 7.
    Yager, R.R., Filev, D.: Operations for granular computing: mixing words with numbers. In: Proceeding 1998 IEEE International Conference Fuzz System, pp. 123–128 (1998)Google Scholar
  8. 8.
    Chen, C.L.P., Zhang, C.: Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf Sci. 275, 314–347 (2014)CrossRefGoogle Scholar
  9. 9.
    Miao, D., Wang, G., Liu, Q., Lin, T., Yao, Y.: Granular Computing: Past, Present and Prospects. Science Press, Beijing (2007)Google Scholar
  10. 10.
    Yao, J., Vasilakos, A., Pedrycz, W.: Granular computing: perspectives and challenges. IEEE Trans. Cybern. 43, 1977–1989 (2013)CrossRefGoogle Scholar
  11. 11.
    Skowron, A., Wasilewski, P.: Information systems in modeling interactive computations on granules. Theor. Comput. Sci. 412, 5939–5959 (2011)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Yao, Y.: Granular Computing: basic issues and possible solutions. In: Proceeding of 5th Joint Conference on Information Science, vol. I, pp. 186–189. Atlantic (2000)Google Scholar
  13. 13.
    Wang, G., Xu, J.: Granular computing with multiple granular layers for brain big data processing. Brain Inform. 1, 1–10 (2014)CrossRefGoogle Scholar
  14. 14.
    Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefGoogle Scholar
  15. 15.
    Zadeh, L.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzz Sets Syst. 90, 111–127 (1997)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Zadeh, L.: Is there a need for fuzzy logic? Inf. Sci. 178, 2751–2779 (2008)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)CrossRefGoogle Scholar
  18. 18.
    Zhang, B., Zhang, L.: Theory of Problem Solving and its Applications, 2nd edn. Tsinghua University Press, Beijing (2007). in ChineseGoogle Scholar
  19. 19.
    Zhang, L., Zhang, B.: The quotient space theory of problem solving. Fund inf. 59, 287–298 (2004)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Wu, D., Ban, X., Oquendo, F.: An architecture model of distributed simulation system based on quotient space. Appl Math. 6, 603S–609S (2012)Google Scholar
  21. 21.
    Dong, Q., et al.: Algebraic properties and topological properties of the quotient space of fuzzy numbers based on Mar\(\breve{e}\) equivalence relation. Fuzz Sets Syst. 245, 63–82 (2014)CrossRefGoogle Scholar
  22. 22.
    Zhang, L., Zhang, B.: Fuzzy reasoning model under quotient space structure. Inf. Sci. 173, 353–364 (2005)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Zhang, Q., Wang, G., Liu, X.: Hierarchical structure analysis of fuzzy quotient space. PR&AI. 21, 627–634 (2008)Google Scholar
  24. 24.
    Zhang, Q.: Research on Hierarchy Granular Computing Theory and its Application[D]. Southwest Jiaotong University, Chengdu (2009)Google Scholar
  25. 25.
    Zhang, Q., Wang, G.: The uncertainty measure of hierarchical quotient space structure. Math. Prob. Eng. 6, 505–515 (2011)MathSciNetGoogle Scholar
  26. 26.
    Li, D., Du, Y.: Artificial Intelligence with Uncertainty. Chapman & Hall/CRC Press, Boca Raton (2008)zbMATHGoogle Scholar
  27. 27.
    Liu, Y., Li, D., He, W., Wang, G.: Granular computing based on gaussian cloud transformation. Fund. Inf. 127, 385–398 (2013)Google Scholar
  28. 28.
    Nakatsuji, M., Fujiwara, Y.: Linked taxonomies to capture users’ subjective assessments of items to facilitate accurate collaborative filtering. Artif. Intell. 207, 52–68 (2014)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Pedrycz, W., Homenda, W.: Building the fundamentals of granular computing: a principle of justifiable granularity. Appl. Soft. Comput. 13, 4209–4218 (2013)CrossRefGoogle Scholar
  30. 30.
    Pedrycz, W.: Allocation of information granularity in optimization and decision-making models: towards building the foundations of granular computing. Eur. J. Oper. Res. 232, 137–145 (2014)CrossRefGoogle Scholar
  31. 31.
    McCalla, G., Greer, J., Barrie, B.: Granularity hierarchies. Comput. Math. App. 23, 363–375 (1992)zbMATHGoogle Scholar
  32. 32.
    Zhu, P., Hu, Q.: Adaptive neighborhood granularity selection and combination based on margin distribution optimization. Inf. Sci. 249, 1–12 (2013)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Yao, Y.: A partition model of granular computing. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B., Swiniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I, pp. 232–253. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  34. 34.
    Qian, Y., Liang, J., Yao, Y., Dang, C.: MGRS: a multi-granulation rough set. Inf. Sci. 180, 949–970 (2010)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Chen, H., Li, T., Luo, C., Hong, S., Wang, G.: A rough set-based method for updating decision rules on attribute values’ coarsening and refining. IEEE Trans. Knowl. Data Eng. 26, 2886–2899 (2014)CrossRefGoogle Scholar
  36. 36.
    Zadeh, L.A.: Fuzzy logic=computing with words. IEEE Trans. Fuzz Syst. 4, 103–111 (1996)CrossRefGoogle Scholar
  37. 37.
    Zadeh, L.A.: From computing with numbers to computing with words-from manipulation of measurements to manipulation of perceptions. IEEE Trans. Circ. Syst-I: Fund Theor. Appl. 45, 105–119 (1999)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Tang, X.Q., Zhu, P., Cheng, J.X.: Cluster analysis based on fuzzy quotient space. J. Softw. 19, 861–868 (2008)MathSciNetCrossRefGoogle Scholar
  39. 39.
    Zhang, C.: Fuzzy sets and quotient spaces. In: Proceedings of the IEEE International Conference on Granular Computing, pp. 350–353 (2005)Google Scholar
  40. 40.
    Liang, J., Chin, K.S., Dang, C., Yam, R.C.M.: A new method for measuring uncertainty and fuzziness in rough set theory. Int. J. Gen. Syst. 31, 331–342 (2002)MathSciNetCrossRefGoogle Scholar
  41. 41.
    Wierman, M.J.: Measuring uncertainty in rough set theory. Int. J. Gen. Syst. 28, 283–297 (1999)MathSciNetCrossRefGoogle Scholar
  42. 42.
    Tang, X., Zhu, P., Cheng, J.: Cluster analysis based on fuzzy quotient space. J. Softw. 19, 861–868 (2008)MathSciNetCrossRefGoogle Scholar
  43. 43.
    Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344, 1492–1496 (2014)CrossRefGoogle Scholar
  44. 44.
    Xu, J., Wang, G., Yu, H.: Review of big data processing based on granular computing. Chin. J. Comput. 38, 1497–1517 (2015)MathSciNetGoogle Scholar

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Authors and Affiliations

  • Guoyin Wang
    • 1
    Email author
  • Ji Xu
    • 2
    • 3
  • Qinghua Zhang
    • 1
  • Yuchao Liu
    • 4
  1. 1.Chongqing Key Laboratory of Computational IntelligenceChongqing University of Posts and TelecommunicationsChongqingChina
  2. 2.School of Information Science and TechnologySouthwest Jiaotong UniversityChengduChina
  3. 3.Institute of Electronic Information TechnologyChongqing Institute of Green and Intelligent Technology, CASChongqingChina
  4. 4.Chinese Institute of Command and ControlBeijingChina

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