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International Conference on Neural Information Processing

ICONIP 2018: Neural Information Processing pp 582–592Cite as

Dynamic Maintenance of Decision Rules for Decision Attribute Values’ Changing

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11303))

Abstract

Rule induction method based on rough set theory (RST) which can generate a minimal set of decision rules by using attribute reduction and approximations has received much attention recently. In real-life, the variation of objects, attributes and attributes’ values affects reducts and approximations, e.g., the coarsening and refining of attributes’ values. The goal of this paper is dynamic maintenance of decision rules for decision attribute values’ coarsening and refining. Two incremental rough-set based methods are proposed to deal with this issue by updating assignment discernibility matrix dynamically without recomputing the reducts from the beginning, which increases the efficiency.

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Notes

  1. 1.

    http://archive.ics.uci.edu/ml/.

References

  1. Ananthanarayana, V., Murty, M.N., Subramanian, D.: Tree structure for efficient data mining using rough sets. Pattern Recognit. Lett. 24(6), 851–862 (2003)

    Article  Google Scholar 

  2. BŁaszczyński, J., Greco, S., SŁowiński, R.: Inductive discovery of laws using monotonic rules. Eng. Appl. Artif. Intell. 25(2), 284–294 (2012)

    Article  Google Scholar 

  3. Bouchet, A., Pastore, J., Ballarin, V.: Segmentation of medical images using fuzzy mathematical morphology. J. Comput. Sci. Technol. 7(3), 256–262 (2007)

    Google Scholar 

  4. Chan, C.C.: A rough set approach to attribute generalization in data mining. Inf. Sci. 107(1–4), 169–176 (1998)

    Article  MathSciNet  Google Scholar 

  5. Chen, H., Li, T., Luo, C., Horng, S.J., Wang, G.: A decision-theoretic rough set approach for dynamic data mining. IEEE Trans. Fuzzy Syst. 23(6), 1958–1970 (2015)

    Article  Google Scholar 

  6. Chen, H., Li, T., Luo, C., Horng, S., Wang, G.: A rough set-based method for updating decision rules on attribute values’ coarsening and refining. IEEE Trans. Knowl. Data Eng. 26(12), 2886–2899 (2014)

    Article  Google Scholar 

  7. Chen, H., Li, T., Qiao, S., Da, R.: A rough set based dynamic maintenance approach for approximations in coarsening and refining attribute values. Int. J. Intell. Syst. 25(10), 1005–1026 (2010)

    Article  Google Scholar 

  8. Hu, C., Liu, S., Huang, X.: Dynamic updating approximations in multigranulation rough sets while refining or coarsening attribute values. Knowl.-Based Syst. 130, 62–73 (2017)

    Article  Google Scholar 

  9. Li, S., Li, T., Liu, D.: Dynamic maintenance of approximations in dominance-based rough set approach under the variation of the object set. Int. J. Intell. Syst. 28(8), 729–751 (2013)

    Article  MathSciNet  Google Scholar 

  10. Liu, D., Li, T., Chen, H., Ji, X.: Approaches to knowledge incremental learning based on the changes of attribute values. In: Intelligent Decision Making Systems - International ISKE Conference on Intelligent Systems and Knowledge Engineering, pp. 94–99 (2010)

    Google Scholar 

  11. Liu, D., Li, T., Zhang, J.: Incremental updating approximations in probabilistic rough sets under the variation of attributes. Knowl.-Based Syst. 73(81), 81–96 (2015)

    Article  Google Scholar 

  12. Pal, S.K., Mitra, P.: Multispectral image segmentation using the rough-set-initialized EM algorithm. IEEE Trans. Geosci. Remote. Sens. 40(11), 2495–2501 (2002)

    Article  Google Scholar 

  13. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)

    Article  Google Scholar 

  14. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)

    Article  MathSciNet  Google Scholar 

  15. Qian, Y., Liang, J., Pedrycz, W., Dang, C.: An efficient accelerator for attribute reduction from incomplete data in rough set framework. Pattern Recognit. 44(8), 1658–1670 (2011)

    Article  Google Scholar 

  16. Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recognit. Lett. 24(6), 833–849 (2003)

    Article  Google Scholar 

  17. Yu, J., Chen, M., Xu, W.: Dynamic computing rough approximations approach to time-evolving information granule interval-valued ordered information system. Appl. Soft Comput. 60, 18–29 (2017)

    Article  Google Scholar 

  18. Zhang, W.X., Mi, J.S., Wu, W.Z.: Approaches to knowledge reductions in inconsistent systems. Int. J. Intell. Syst. 18(9), 989–1000 (2003)

    Article  Google Scholar 

  19. Zheng, Z., Wang, G., Wu, Y.: A rough set and rule tree based incremental knowledge acquisition algorithm. Fundam. Inform. 59(2–3), 299–313 (2004)

    MathSciNet  MATH  Google Scholar 

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Acknowledgment

This work was partially supported by the National Natural Science Foundation of China (Nos. 61473259, 61502335, 61070074, 60703038) and the Hunan Provincial Science & Technology Program Project (2018TP1018, 2018RS3065).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin University, Tianjin, 300350, China

    Yingyao Wang & Hong Shi

  2. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China

    Jianhua Dai

Authors
  1. Yingyao Wang
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  2. Jianhua Dai
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  3. Hong Shi
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Corresponding author

Correspondence to Jianhua Dai .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Wang, Y., Dai, J., Shi, H. (2018). Dynamic Maintenance of Decision Rules for Decision Attribute Values’ Changing. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11303. Springer, Cham. https://doi.org/10.1007/978-3-030-04182-3_51

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  • DOI: https://doi.org/10.1007/978-3-030-04182-3_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04181-6

  • Online ISBN: 978-3-030-04182-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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