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Incremental Updating Rough Approximations in Interval-valued Information Systems

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Book cover Rough Sets and Knowledge Technology (RSKT 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9436))

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Abstract

Interval-valued Information System (IvIS) is a generalized model of single-valued information system, in which the attribute values of objects are all interval values instead of single values. The attribute set in IvIS is not static but rather dynamically changing over time with the collection of new information. The rough approximations may evolve accordingly, which should be updated continuously for data analysis based on rough set theory. In this paper, on the basis of the similarity-based rough set theory in IvIS, we first analyze the relationships between the original approximation sets and the updated ones. And then we propose the incremental methods for updating rough approximations when adding and removing attributes, respectively. Finally, a comparative example is used to validate the effectiveness of the proposed incremental methods.

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Acknowledgements

This work is supported by the National Science Foundation of China (No. 61175047), NSAF (No. U1230117), the Young Software Innovation Foundation of Sichuan Province, China (No. 2014-046) and the Beijing Key Laboratory of Traffic Data Analysis and Mining (BKLTDAM2014001).

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Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China

    Yingying Zhang, Tianrui Li, Chuan Luo & Hongmei Chen

  2. Key Lab of Cloud Computing and Intelligent Technique, Sichuan Province, Chengdu, 611756, China

    Yingying Zhang, Tianrui Li, Chuan Luo & Hongmei Chen

Authors
  1. Yingying Zhang
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  2. Tianrui Li
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  3. Chuan Luo
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  4. Hongmei Chen
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Corresponding author

Correspondence to Yingying Zhang .

Editor information

Editors and Affiliations

  1. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  2. Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

  3. Indian Statistical Institute, Kolkata, India

    Sushmita Mitra

  4. Zhejiang Ocean University, Zhejiang, China

    Wei-Zhi Wu

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Zhang, Y., Li, T., Luo, C., Chen, H. (2015). Incremental Updating Rough Approximations in Interval-valued Information Systems. In: Ciucci, D., Wang, G., Mitra, S., Wu, WZ. (eds) Rough Sets and Knowledge Technology. RSKT 2015. Lecture Notes in Computer Science(), vol 9436. Springer, Cham. https://doi.org/10.1007/978-3-319-25754-9_22

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  • DOI: https://doi.org/10.1007/978-3-319-25754-9_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25753-2

  • Online ISBN: 978-3-319-25754-9

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