Matrix-Based Rough Set Approach for Dynamic Probabilistic Set-Valued Information Systems
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  • 关键词:Information systems ; Rough sets ; Incremental learning ; Matrix
  • 刊名:Lecture Notes in Computer Science
  • 出版年:2016
  • 出版时间:2016
  • 年:2016
  • 卷:9920
  • 期:1
  • 页码:197-206
  • 全文大小:220 KB
  • 参考文献:1.Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)MathSciNet CrossRef MATH
    2.Maji, P.: A rough hypercuboid approach for feature selection in approximation spaces. IEEE Trans. Knowl. Data Eng. 26, 16–29 (2014)CrossRef
    3.Kotlowski, W., Slowinski, R.: On nonparametric ordinal classification with monotonicity constraints. IEEE Trans. Knowl. Data Eng. 25, 2576–2589 (2013)CrossRef
    4.Yao, Y.: Rough sets and three-way decisions. In: Ciucci, D., Wang, G., Mitra, S., Wu, W.-Z. (eds.) RSKT 2015. LNCS (LNAI), vol. 9436, pp. 62–73. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-25754-9_​6 CrossRef
    5.Guan, Y.Y., Wang, H.K.: Set-valued information systems. Inf. Sci. 176, 2507–2525 (2006)MathSciNet CrossRef MATH
    6.Qian, Y.H., Dang, C.Y., Liang, J.Y., Tang, D.W.: Set-valued ordered information systems. Inf. Sci. 179, 2809–2832 (2009)MathSciNet CrossRef MATH
    7.Ziarko, W.: Variable precision rough set model. J. Comput. Syst. Sci. 46, 39–59 (1993)MathSciNet CrossRef MATH
    8.Li, T.R., Ruan, D., Geert, W., Song, J., Xu, Y.: A rough sets based characteristic relation approach for dynamic attribute generalization in data mining. Knowl. Based Syst. 20, 485–494 (2007)CrossRef
    9.Liang, J.Y., Wang, F., Dang, C.Y., Qian, Y.H.: A group incremental approach to feature selection applying rough set technique. IEEE Trans. Knowl. Data Eng. 26, 294–308 (2014)CrossRef
    10.Yang, X.B., Qi, Y., Yu, H.L., Song, X.N., Yang, J.Y.: Updating multigranulation rough approximations with increasing of granular structures. Knowl. Based Syst. 64, 59–69 (2014)CrossRef
    11.Luo, C., Li, T.R., Chen, H.M., Lu, L.X.: Fast algorithms for computing rough approximations in set-valued decision systems while updating criteria values. Inf. Sci. 299, 221–242 (2015)MathSciNet CrossRef
    12.Zhang, J.B., Li, T.R., Ruan, D., Liu, D.: Rough sets based matrix approaches with dynamic attribute variation in set-valued information systems. Int. J. Approximate Reasoning 53, 620–635 (2012)MathSciNet CrossRef MATH
    13.Wang, S.P., Zhu, W., Zhu, Q.X., Min, F.: Characteristic matrix of covering and its application to boolean matrix decomposition. Inf. Sci. 263, 186–197 (2014)MathSciNet CrossRef MATH
    14.Huang, Y., Li, T., Horng, S.: Dynamic maintenance of rough fuzzy approximations with the variation of objects and attributes. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds.) RSFDGrC 2015. LNCS (LNAI), vol. 9437, pp. 173–184. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-25783-9_​16 CrossRef
    15.Kailath, T.: The divergence and bhattacharyya distance measures in signal selection. IEEE Trans. Commun. Technol. 15, 52–60 (1967)CrossRef
  • 作者单位:Yanyong Huang (23)
    Tianrui Li (23)
    Chuan Luo (24)
    Shi-jinn Horng (23)

    23. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China
    24. College of Computer Science, Sichuan University, Chengdu, 610056, China
  • 丛书名:Rough Sets
  • ISBN:978-3-319-47160-0
  • 刊物类别:Computer Science
  • 刊物主题:Artificial Intelligence and Robotics
    Computer Communication Networks
    Software Engineering
    Data Encryption
    Database Management
    Computation by Abstract Devices
    Algorithm Analysis and Problem Complexity
  • 出版者:Springer Berlin / Heidelberg
  • ISSN:1611-3349
  • 卷排序:9920
文摘
Set-valued information systems (SvIS), in which the attribute values are set-valued, are important types of data representation with uncertain and missing information. However, all previous investigations in rough set community do not consider the attribute values with probability distribution in SvIS, which may be impractical in many real applications. This paper introduces probabilistic set-valued information systems (PSvIS) and presents an extended variable precision rough sets (VPRS) approach based on \(\lambda \)-tolerance relation for PSvIS. Furthermore, due to the dynamic variation of attributes in PSvIS, viz., the addition and deletion of attributes, we present a matrix characterization of the proposed VPRS model and discuss some related properties. Then incremental approaches for maintaining rough approximations based on matrix operations are presented, which can effectively accelerate the updating of rough approximations in dynamic PSvIS.

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