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Knowledge induction from uncertain information systems

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Abstract

Rough set theory is an important method to deal with imprecise and vague knowledge. However, one of the difficult problems of rule induction is that the classic rough set theory cannot extract rules from those information systems which include uncertain continuous attribute values. In this paper, a new rough set approach that integrates fuzzy set theory is presented to induce knowledge in this kind of information system. Fuzzy similarity relationis used as the base of similarity classification of each object in a universe and the definition of upper and lower approximation of an object set X⊆U is proposed. A decision table includes uncertain continuous data and is divided into two subtables for rules induction, one being a consistent decision subtable and the other a completely inconsistent decision subtable; two different new methods for the two subtables are proposed to induce decision rules. At the end of the paper, an example is given for further illustration.

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References

  1. Pawlak Z (2000) AI and intelligent industrial applications: the rough set perspective. Cybern Syst Int J 31:227–252

    Article  MATH  Google Scholar 

  2. Pawlak Z (1985) Rough sets and fuzzy sets. Fuzzy Sets Syst 17:99–102

    Article  MATH  MathSciNet  Google Scholar 

  3. Mrozek A (1992) Rough sets in computer implementation of rule-based control of industrial process. In: Slowinski R et al (ed) Intelligent decision support. Handbook of application and advances of the rough sets theory. Kluwer, Dordrecht, pp 19–32

    Google Scholar 

  4. Tsumoto S (2003) Automated extraction of hierarchical decision rules from clinical database using rough set model. Expert Syst Appl 24:189–197

    Article  Google Scholar 

  5. Jagielska I, Matthews C, Whitfort T (1999) An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems. Neurocomputing 24:37–54

    Article  MATH  Google Scholar 

  6. Khoo LP, Tor SB, Zhai LY (1999) A rough-set-based approach for classification and rule induction. Int J Adv Manuf Technol 15:438–444

    Article  Google Scholar 

  7. Beaubouef T, Petry FE (2000) Fuzzy rough set techniques for uncertainty processing in a relational database. Int J Intell Syst 15:389–424

    Article  MATH  Google Scholar 

  8. Yasdi R (1995) Combining rough sets learning- and neural learning-method to deal with uncertain and imprecise information. Neurocomputing 7:61–84

    Article  MATH  Google Scholar 

  9. Jagielska I (1998) Hybrid rough sets/neural network approach to the development of a decision support system. In: IEEE International Conference on Neural Networks-Conference Proceedings. IEEE Piscataway, NJ, pp 24–28

  10. Wang J, Liang J (2002) Rough set and rough classification based on imperfect information systems. In: Proceedings of the World Congress on Intelligent Control and Automation (WCICA). Institute of Electrical and Electronics Engineers, pp 437–440

  11. Jensen R, Qiang S (2004) Fuzzy-rough attribute reduction with application to web categorization. Fuzzy Sets Syst 141:469–485

    Article  MATH  Google Scholar 

  12. Shi F, Lou ZL et al (2003) An improved rough set approach to design of gating scheme for injection moulding. Int J Adv Manuf Technol 21:662–668

    Article  Google Scholar 

  13. Slowinski R, Vanderpooten (1997) Similarity relations as a basis for rough approximations. In: Wang PP (ed) ICS research report 53/95, Warsaw Univ. Technology, 1995. Also in advances in machine intelligence and soft-computing. Bookwrights, Raleigh, NC, pp 17–33

    Google Scholar 

  14. Polkowski L, Skowron A, Zytkow J (1995) Tolerance based rough sets. In: Lin TY, Wildverger AM et al (eds) Soft computing: rough sets, fuzzy logic, neural networks, uncertainty management. Simulation Councils, San Diego, CA, pp 18–21

    Google Scholar 

  15. Marcus S (1994) Tolerance rough sets, Cech topologies, learning processes. Bull Pol Acad Sci Tech Sci 42:471–487

    MATH  Google Scholar 

  16. Slowinski R, Vanderpooten D (2000) A generalized definition of rough approximations based on similarity. IEEE Trans Knowl Data Eng 12:331–336

    Article  Google Scholar 

  17. Dubois D, Prade H (1999) Rough fuzzy sets and fuzzy rough sets. Int J Gen Syst 17:191–208

    Article  Google Scholar 

  18. Pawlak Z (1992) Rough sets, theoretical aspects of reasoning about data. Klumer Academic, Warsaw

    Google Scholar 

  19. Liu Q, Wang Q (1996) Rough number based on rough set and logical values of λ operator (in Chinese). Journal of Software Supplement: 455–461

  20. Liu Q (1996) Accuracy operator rough logic and its resolution reasoning. In: Proceeding of RSFD’ 96 International Conference on Artificial Intelligence. University of Tokyo, Tokyo, pp 55–59

    Google Scholar 

  21. Liu Q, Huang Z et al (1999) Decision rules with rough operator and soft computing of data mining. J Comput Res Dev 7:800–804

    MathSciNet  Google Scholar 

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Acknowledgements

Thanks to my professors for their painstaking efforts on behalf of my paper. Thanks to all the people who cared about my paper for their helpful comments.

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Authors and Affiliations

  1. Department of Plasticity Forming Engineering, Shanghai Jiaotong University, Shanghai, 200030, People’s Republic of China

    Jiang Yajun & Lou Zhenliang

  2. Shanghai Jiaotong University, 1954 Huashan Road, P.O. Box 165, Shanghai, 200030, P.R. China

    Jiang Yajun

Authors
  1. Jiang Yajun
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  2. Lou Zhenliang
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Correspondence to Jiang Yajun.

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Yajun, J., Zhenliang, L. Knowledge induction from uncertain information systems. Int J Adv Manuf Technol 30, 769–777 (2006). https://doi.org/10.1007/s00170-005-0117-7

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  • DOI: https://doi.org/10.1007/s00170-005-0117-7

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