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Rough Neural Network Based on Bottom-Up Fuzzy Rough Data Analysis

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Abstract

Based on bottom-up fuzzy rough data analysis, a new rough neural network decision-making model is proposed. Through supervised Gaustafason–Kessel (G–K) clustering algorithm, proper fuzzy clusters are found to partition the input data space. At the same time cluster number is searched by monotone increasing process. If the cluster number matches with that exactly exist in data sets then excellent fuzzy rough data modeling (FRDM) model can be built. And by integrating it with neural network technique, corresponding rough neural network is constructed. Our method overcomes the defects of conventional top-down based rough logic neural network (RLNN) method, and it also achieves adaptive learning ability and comprehensive soft decision-making ability compared with FRDM model. The experiment results indicate that our method has stronger generalization ability and more compact network structure than conventional RLNN.

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References

  1. Pawlak Z (1998) Rough set theory and its application to data analysis. Cybern Syst 29(9): 661–688. doi:10.1080/019697298125470

    Article  MATH  MathSciNet  Google Scholar 

  2. Pal SK (2004) Soft data mining, computational theory of perceptions, and rough-fuzzy approach. Inf Sci 163(1–3): 5–12. doi:10.1016/j.ins.2003.03.014

    Article  Google Scholar 

  3. Beaubouef T, Ladner R, Petry F (2004) Rough set spatial data modeling for data mining. Int J Intell Syst 19(7): 567–584. doi:10.1002/int.20019

    Article  MATH  Google Scholar 

  4. Zeng CH, Xu Y, Zheng P, Xie WC (2005) Knowledge discovery for goods classification based on rough set. In: IEEE international conference on granular computing, Beijing, pp 334–337

  5. Shao XY, Wang ZH, Li PG et al (2006) Integrating data mining and rough set for customer group-based discovery of product configuration rules. Int J Prod Res 44(14/15): 2789–2811. doi:10.1080/00207540600675777

    Article  MATH  Google Scholar 

  6. Nguyen HS (2005) Approximate Boolean reasoning approach to rough sets and data mining. Lecture Notes in Computer Science. Springer, Berlin, LNCS 3642, pp 12–22

  7. Jiang YJ, Chen J, Ruan XY (2006) Fuzzy similarity-based rough set method for case-based reasoning and its application in tool selection. Int J Mach Tools Manuf 46(2): 107–113. doi:10.1016/j.ijmachtools.2005.05.003

    Article  Google Scholar 

  8. Pal SK, Shankar BU, Mitra P (2005) Granular computing, rough entropy and object extraction. Pattern Recognit Lett 26(16): 2509–2517. doi:10.1016/j.patrec.2005.05.007

    Article  Google Scholar 

  9. Liu Q, Wang JY (2006) Semantic analysis of rough logical formulas based on granular computing. In: IEEE international conference on granular computing, Atlanta, pp 393–396

  10. Zhang DB, Wang YN, Yi LZ (2005) Rough neural network and its application to intelligent information processing. Contr Decis 20(1): 121–126

    Google Scholar 

  11. Li K, Liu YS (2005) Rough set based fuzzy neural network algorithm for weather prediction. In: International conference on machine learning and cybernetics, Guangzhou, pp 1888–1892

  12. Yu CY, Wu MH, Wu M (2003) Combining rough set theory with neural network theory for pattern recognition. In: International conference on robotics, intelligent systems and signal processing, Changsha, pp 880–885

  13. Liu HJ, Tuo HY, Liu YC (2004) Rough neural network of variable precision. Neural Process Lett 19(1): 73–87. doi:10.1023/B:NEPL.0000016851.47914.40

    Article  Google Scholar 

  14. Wu ZC (2001) Research on remote sensing image classification using neural network based on rough sets[C]. In: Proceedings of info-tech and info-net, Beijing, vol 4, pp 279–284

  15. Kowalczyk W (1996) Analyzing temporal patterns with rough sets. In: Proceedings of the 4th European congress on intelligent techniques and soft computing (EUFIT’96), Aachen, pp 139–143

  16. Kowalczyk W (1998) Rough data modeling: a new technique for analyzing data. In: Polkowski L, Skowron A (eds) Rough sets in knowledge discovery 1: methodology and applications. Physica-Verlag, Heidelberg, pp 400–421

    Google Scholar 

  17. Pista Z, Lenarcik A (1998) Learning rough classifiers from large database with missing values. In: Polkowski L, Skowron A (eds) Rough sets and knowledge discovery 1: methodology and applications. Physica-Verlag, Heidelberg, pp 483–499

    Google Scholar 

  18. Mollestad T, Komorowski J (1998) A rough set framework for data mining of propositional default rules. In: Pal SK, Skowron A (eds) Rough-fuzzy hybridization: a new trend in decision making. Springer, Singapore, pp 298–316

    Google Scholar 

  19. Greco S, Matarazzo B, SŁowinski R (1998) New developments in the rough set approach to multi-attribute decision analysis. Bull Int Rough set Soc 2: 57–87

    Google Scholar 

  20. Huang JJ, Wu JF, Cai YZ (2005) Fuzzy rough data model: a new technique for analyzing data. Chin J Comput 28(11): 1866–1874

    Google Scholar 

  21. Huang JJ, Li SY, Cai YZ (2005) An apporach to building rough data model through supervised fuzzy clustering. J Softw 16(5): 744–753. doi:10.1360/jos160744

    Article  MATH  Google Scholar 

  22. Loken T (1999) Rough modeling: extracting compact models from large database. Master Thesis, Norwegian University of Science and Technology, pp 57–81

  23. Loken T, Komorowski J (2001) Rough modeling—a bottom-up approach to model construction. Int J Appl Math Comput Sci 11(3): 675–690

    MathSciNet  Google Scholar 

  24. Haykin S (2004) Neural networks: a comprehensive foundation, 2nd edn. China Machine Press, Beijing, pp 183–220

    Google Scholar 

  25. Tuceryan M, Jain AK (1993) Texture analysis. Handbook of pattern recognition and computer vision. World Scientific, Singapore, pp 235–276

    Google Scholar 

  26. Randen T, Hushy JH (1999) Filtering for texture classification: a comparative study. IEEE Trans Pattern Anal Mach Intell 21(4): 291–310. doi:10.1109/34.761261

    Article  Google Scholar 

  27. Hong ZQ (1991) Algebraic feature extraction of image for recognition. Pattern Recognit 24(3): 211–219. doi:10.1016/0031-3203(91)90063-B

    Article  Google Scholar 

  28. Swiniarski RW, Hargis L (2001) Rough sets as a front end of neural-network texture classifiers. Neurocomputing 36(1–4): 85–102. doi:10.1016/S0925-2312(00)00337-4

    Article  MATH  Google Scholar 

  29. Targhi AT, Shademan A (2003) Clustering of singular value decomposition of image data with applications to texture classification. In: Proceedings of SPIE 2003, Switzerland, pp 972–979

  30. Li ST, Kwok JT, Zhu HL, Wang YN (2003) Texture classification using the support vector machines. Pattern Recognit 36(12): 2883–2893. doi:10.1016/S0031-3203(03)00219-X

    Article  MATH  Google Scholar 

  31. Martin TH, Howard BD, Mark HB (2004) Neural network design. China Machine Press, Beijing, pp 295–299

    Google Scholar 

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Authors and Affiliations

  1. Institute of Information Engineering, Xiangtan University, 411105, Xiangtan, China

    Dongbo Zhang

  2. College of Electrical and Information Engineering, Hunan University, 410082, Changsha, China

    Yaonan Wang

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  1. Dongbo Zhang
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  2. Yaonan Wang
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Correspondence to Dongbo Zhang.

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Zhang, D., Wang, Y. Rough Neural Network Based on Bottom-Up Fuzzy Rough Data Analysis. Neural Process Lett 30, 187–211 (2009). https://doi.org/10.1007/s11063-009-9118-0

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  • DOI: https://doi.org/10.1007/s11063-009-9118-0

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