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A Novel Attribute Reduction Approach Based on Improved Attribute Significance

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Computational Intelligence and Intelligent Systems (ISICA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 873))

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Abstract

Aiming at the limitations of attribute reduction approach based on Pawlak attribute significance and conditional entropy, an efficient attribute reduction algorithm based on distinguish matrix and the improved attribute significance is put forward. Firstly, the deficiencies of two kinds of classical attribute reduction are analyzed. Furthermore, an improved attribute significance definition is given according to ability to distinguish one object from another in the universe, and then it is used to calculate the significance of attribute in discernibility matrix; Finally, a minimum attribute reduction can be gained by adding attribute to the core attribute set one by one according to descending order of attribute significance. Analysis on numerical example shows that the proposed algorithm can find the minimal attribute reduction effectively. Compared with the previous algorithm, the proposed algorithm can reduce the calculation amount on reduction greatly in the decision table which has more conditional attributes.

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References

  1. Dai, J., Han, H., Hu, Q., et al.: Discrete particle swarm optimization approach for cost sensitive attribute reduction. Knowl. Based Syst. 102(6), 116–126 (2016)

    Article  Google Scholar 

  2. Teng, S., Lu, M., Yang, A., et al.: Efficient attribute reduction from the viewpoint of discernibility. Inf. Sci. 326(1), 297–314 (2016)

    Article  MathSciNet  Google Scholar 

  3. Qian, Y., Liang, J., Pedrycz, W., et al.: Positive approximation: an accelerator for attribute reduction in rough set theory. Artif. Intell. 174(9–10), 597–618 (2010)

    Article  MathSciNet  Google Scholar 

  4. Yao, Y., Zhao, Y.: Attribute reduction in decision-theoretic rough set models. Inf. Sci. 178(17), 3356–3373 (2013)

    Article  MathSciNet  Google Scholar 

  5. Bing, X., Liam, C., Lin, S., et al.: Binary PSO and rough set theory for feature selection: a multi-objective filter based approach. Int. J. Comput. Intell. Appl. 13(2), 1157–1948 (2014)

    Google Scholar 

  6. Pawlak, Z.: Rough set theory and its applications to data analysis. J. Cybern. 29(7), 661–688 (1998)

    MATH  Google Scholar 

  7. Yang, C., Ge, H., Wang, Z.: Overview of attribute reduction on rough set. Appl. Res. Comput. 29(1), 16–20 (2012)

    Google Scholar 

  8. Qian, W., Yang, B., Xie, Y., et al.: A quick algorithm for attribute reduction based on attribute measure. J. Chin. Comput. Syst. 35(6), 1407–1411 (2014)

    Google Scholar 

  9. Zhang, T., Xiao, J., Wang, X.: Algorithms of attribute relative reduction in rough set theory. Acta Electronica Sinica 33(11), 2080–2083 (2005)

    Google Scholar 

  10. Liao, Q., Long, P.: Rough set reduction method of attribute based on importance of attribute. Comput. Eng. Appl. 49(15), 130–132 (2013)

    Google Scholar 

  11. Yang, M.: Approximate reduction based on conditional information entropy in decision tables. Acta Electronica Sinica 35(11), 2156–2160 (2007)

    Google Scholar 

  12. Cao, F., Liang, J., Qian, Y.: Decision table reduction based on information entropy. Comput. Appl. 25(11), 2630–2631 (2005)

    Google Scholar 

  13. Liu, Q., Li, F., Min, F., et al.: An efficient knowledge reduction algorithm based on new conditional information entropy. Control Decis. 20(8), 878–882 (2005)

    MATH  Google Scholar 

  14. Li, M., Huang, W., Liu, Z.: Improved CEBARKNC on decision table reduction. J. Comput. Appl. 26(4), 864–866 (2006)

    Google Scholar 

  15. Zhang, W.: Rough Set Theory and Methods, 1st edn. Science Press, Beijing (2001)

    Google Scholar 

  16. Liu, Q.: Rough Set and Rough Reasoning, 1st edn. Science Press, Beijing (2001)

    Google Scholar 

  17. Ye, J., Wang, L.: An approach of ascertaining the combinatorial attribute weight based on distinguish matrix. J. Comput. Sci. 41(11), 273–277 (2014)

    Google Scholar 

  18. Miao, D., Li, D.: Rough Sets Theory Algorithms and Application, 1st edn. Tsinghua University Press, Beijing (2008)

    Google Scholar 

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Acknowledgements

The research is supported by the Natural Science Foundation of China under grant Nos. 61461032, 61363047, 61562061 and 51669014. The Natural Science Foundation of Jiangxi Province of China under grant Nos. 20151BAB207067 and 20151BAB207032.

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Authors and Affiliations

  1. School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Jun Ye & Lei Wang

Authors
  1. Jun Ye
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  2. Lei Wang
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Corresponding author

Correspondence to Jun Ye .

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Editors and Affiliations

  1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. Jiangxi University of Science and Technology, Ganzhou, Jiangxi, China

    Wei Li

  3. Chemical and Petroleum Engineering, University of Calgary, Calgary, Alberta, Canada

    Zhangxing Chen

  4. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

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Ye, J., Wang, L. (2018). A Novel Attribute Reduction Approach Based on Improved Attribute Significance. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-13-1648-7_5

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  • DOI: https://doi.org/10.1007/978-981-13-1648-7_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1647-0

  • Online ISBN: 978-981-13-1648-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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