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Fuzzy Set-Valued Information Systems and the Algorithm of Filling Missing Values for Incomplete Information Systems

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  • Zhaohao Wang
  • Xiaoping Zhang

Abstract

How to effectively deal with missing values in incomplete information systems (IISs) according to the research target is still a key issue for investigating IISs. If the missing values in IISs are not handled properly, they will destroy the internal connection of data and reduce the efficiency of data usage. In this paper, in order to establish effective methods for filling missing values, we propose a new information system, namely, a fuzzy set-valued information system (FSvIS). By means of the similarity measures of fuzzy sets, we obtain several binary relations in FSvISs, and we investigate the relationship among them. This is a foundation for the researches on FSvISs in terms of rough set approach. Then, we provide an algorithm to fill the missing values in IISs with fuzzy set values. In fact, this algorithm can transform an IIS into an FSvIS. Furthermore, we also construct an algorithm to fill the missing values in IISs with set values (or real values). The effectiveness of these algorithms is analyzed. The results showed that the proposed algorithms achieve higher correct rate than traditional algorithms, and they have good stability. Finally, we discuss the importance of these algorithms for investigating IISs from the viewpoint of rough set theory.

Suggested Citation

  • Zhaohao Wang & Xiaoping Zhang, 2019. "Fuzzy Set-Valued Information Systems and the Algorithm of Filling Missing Values for Incomplete Information Systems," Complexity, Hindawi, vol. 2019, pages 1-17, December.
    • Handle: RePEc:hin:complx:3213808
    DOI: 10.1155/2019/3213808
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    References listed on IDEAS

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    1. Chen J. & Shao J., 2001. "Jackknife Variance Estimation for Nearest-Neighbor Imputation," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 260-269, March.
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