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A Relative Tolerance Relation of Rough Set for Incomplete Information Systems

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Recent Advances on Soft Computing and Data Mining (SCDM 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 700))

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Abstract

Rough set theory is an effective approach to imprecision, vagueness, and uncertainty. This theory overlaps with many other theories such that fuzzy sets, evidence theory, and statistics. From a practical point of view, it is a good tool for data analysis. However, classical rough set theory cannot cope with the incomplete information systems where some attribute values are missing. There have been efforts in studying incomplete information systems for data classification which are based on the extensions of rough set theory. Moreover, the existing approaches have their weaknesses in terms of inflexible and imprecise in data classifications. To overcome these issues, we propose a relative tolerance relation of rough set (RTRS) to handling incomplete information systems, which it has flexibility and precisely for data classification. We compared RTRS with the existing approaches, the results show that our proposed method relatively achieves higher flexibility and precisely in data classification in incomplete information systems.

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References

  1. Bunting, B.P., Adamson, G., Mulhall, P.K.: A Monte Carlo examination of an MTMM model with planned incomplete data structures. Struct. Equ. Model. 9, 369–389 (2002)

    Article  MathSciNet  Google Scholar 

  2. Chmielewski, M.R., Grzymala-Busse, J.W., Peterson, N.W., Than, S.: The rule induction system LERS-a version for personal computers. Found. Comput. Decis. Sci. 18, 181–212 (1993)

    MATH  Google Scholar 

  3. Kryszkiewicz, M.: Rough set approach to incomplete information systems. Inf. Sci. 112, 39–49 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  4. Kryszkiewicz, M.: Rules in incomplete information systems. Inf. Sci. 113, 271–292 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  5. Zhou, J., Yang, X.: Rough set model based on hybrid tolerance relation. In: International Conference on Rough Sets and Knowledge Technology, pp. 28–33. Springer (2012)

    Google Scholar 

  6. Zhou, Q.: Research on tolerance-based rough set models. In: 2010 International Conference on System Science, Engineering Design and Manufacturing Informatization (ICSEM), pp. 137–139. IEEE (2010)

    Google Scholar 

  7. Yang, X.: An improved model of rough sets on incomplete information systems. In: International Conference on Management of e-Commerce and e-Government, 2009. ICMECG’09, pp. 193–196. IEEE (2009)

    Google Scholar 

  8. Stefanowski, J., Tsoukiàs, A.: On the extension of rough sets under incomplete information. In: International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, pp. 73–81. Springer (1999)

    Google Scholar 

  9. Stefanowski, J., Tsoukias, A.: Incomplete information tables and rough classification. Comput. Intell. 17, 545–566 (2001)

    Article  MATH  Google Scholar 

  10. Wu, Y., Guo, Q.: An extension model of rough set in incomplete information system. In: 2010 2nd International Conference on Future Computer and Communication (ICFCC), pp. 2–434. IEEE (2010)

    Google Scholar 

  11. Yang, X., Song, X., Hu, X.: Generalisation of rough set for rule induction in incomplete system. Int. J. Granul. Comput. Rough Sets Intell. Syst. 2, 37–50 (2011)

    Article  Google Scholar 

  12. Wang, G.: Extension of rough set under incomplete information systems. In: Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, 2002. FUZZ-IEEE’02, pp. 1098–1103. IEEE (2002)

    Google Scholar 

  13. Van Nguyen, D., Yamada, K., Unehara, M.: Extended tolerance relation to define a new rough set model in incomplete information systems. Adv. Fuzzy Syst. 2013 9 (2013)

    MathSciNet  MATH  Google Scholar 

  14. Dai, J., Wang, W., Xu, Q., Tian, H.: Uncertainty measurement for interval-valued decision systems based on extended conditional entropy. Knowl.-Based Syst. 27, 443–450 (2012)

    Article  Google Scholar 

  15. Pawlak, Z.: Rough sets. Int. J. Comput. Inform. Sci. 11, 341–356 (1982)

    Article  MATH  Google Scholar 

  16. Parmar, D., Wu, T., Blackhurst, J.: MMR: an algorithm for clustering categorical data using rough set theory. Data Knowl. Eng. 63, 879–893 (2007)

    Article  Google Scholar 

  17. Herawan, T., Deris, M.M., Abawajy, J.H.: A rough set approach for selecting clustering attribute. Knowl.-Based Syst. 23, 220–231 (2010)

    Article  Google Scholar 

  18. Lichman, M.: UCI Machine Learning Repository. http://archive.ics.uci.edu/ml (2013)

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Authors and Affiliations

  1. School of Industrial Engineering, Telkom University, 40257, Bandung, West Java, Indonesia

    Rd. Rohmat Saedudin & Edi Sutoyo

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia

    Hairulnizam Mahdin & Shahreen Kasim

  3. Department of Information Systems, Universitas Ahmad Dahlan, 55161, Yogyakarta, Indonesia

    Iwan Tri Riyadi Yanto

  4. Software Engineering Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Rohayanti Hassan

Authors
  1. Rd. Rohmat Saedudin
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  2. Hairulnizam Mahdin
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  3. Shahreen Kasim
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  4. Edi Sutoyo
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  5. Iwan Tri Riyadi Yanto
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  6. Rohayanti Hassan
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Corresponding author

Correspondence to Rd. Rohmat Saedudin .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Rozaida Ghazali

  2. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Mustafa Mat Deris

  3. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia

    Nazri Mohd Nawi

  4. School of Information Technology, Deakin University, Geelong, Victoria, Australia

    Jemal H. Abawajy

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Saedudin, R.R., Mahdin, H., Kasim, S., Sutoyo, E., Yanto, I.T.R., Hassan, R. (2018). A Relative Tolerance Relation of Rough Set for Incomplete Information Systems. In: Ghazali, R., Deris, M., Nawi, N., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2018. Advances in Intelligent Systems and Computing, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-319-72550-5_8

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  • DOI: https://doi.org/10.1007/978-3-319-72550-5_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72549-9

  • Online ISBN: 978-3-319-72550-5

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