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Decision rules acquisition based on interval knowledge granules for incomplete ordered decision information systems

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Abstract

For incomplete ordered decision information systems (IODIS), the interval, defined as an intersection of the dominating set of one object and the dominated set of another object, is regarded as the basic knowledge granule used for defining the lower and upper approximations. It is shown in this paper that such knowledge granule can help induce the “at least and at most” decision rules for IODIS, which would assign an object to more precise decision classes. In order to compute the optimal “at least and at most” decision rules, the concept of relative reduct of an interval is proposed, and the corresponding discernibility function is constructed for computing the relative reduct. Finally, an illustrative example is provided to demonstrate the advantages of our method in decision making.

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Acknowledgments

This research is supported by the National Natural Science Foundation of China (61070241) and the Shandong Provincial Natural Science Foundation, China (ZR2013AQ007).

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Authors and Affiliations

  1. School of Mathematical Sciences, University of Jinan, Jinan, 250022, China

    Jilin Huang & Yanyong Guan

  2. Department of Mathematics, Jinan Preschool Education College, Jinan, 250307, China

    Xuezhi Du

  3. School of Mathematical Sciences, University of Jinan, Jinan, 250022, China

    Hongkai Wang

Authors
  1. Jilin Huang
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  2. Yanyong Guan
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  3. Xuezhi Du
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  4. Hongkai Wang
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Correspondence to Yanyong Guan.

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Huang, J., Guan, Y., Du, X. et al. Decision rules acquisition based on interval knowledge granules for incomplete ordered decision information systems. Int. J. Mach. Learn. & Cyber. 6, 1019–1028 (2015). https://doi.org/10.1007/s13042-015-0408-8

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  • DOI: https://doi.org/10.1007/s13042-015-0408-8

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