EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 11 (2017) ITM Web Conf., 11 (2017) 01016 References
Open Access
Issue
ITM Web Conf.
Volume 11, 2017
2017 International Conference on Information Science and Technology (IST 2017)
Article Number 01016
Number of page(s) 10
Section Session I: Computational Intelligence
DOI https://doi.org/10.1051/itmconf/20171101016
Published online 23 May 2017
  1. Z. Pawlak,Rough Sets[J].International Journal of Information Computer Science,1982,11(5):341–356. [CrossRef] [Google Scholar]
  2. Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, System Theory, Knowledge Engineering and Problem Solving, vol. 9, Kluwer Academic Publishers, Dordrecht, 1991. [Google Scholar]
  3. Z. Pawlak, A. Skowron, Rudiments of rough sets, Information Sciences 177 (2007) 3–27. [CrossRef] [MathSciNet] [Google Scholar]
  4. D. Liu, T.R. Li, D. Ruan, J.B. Zhang, Incremental learning optimization on knowledge discovery in dynamic business intelligent systems, Journal of Global Optimization 51 (2011) 325–344, http://dx.doi.org/10.1007/s10898-010-9607-8. [CrossRef] [MathSciNet] [Google Scholar]
  5. J. Qian, D.Q. Miao, Z.H. Zhang, W. Li, Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation, International Journal of Approximate Reasoning 52 (2011) 212–230. [CrossRef] [MathSciNet] [Google Scholar]
  6. Q.H. Hu, D.R. Yu, J.F. Liu, C.X. Wu, Neighborhood rough set based heterogeneous feature subset selection, Information Sciences 178 (2008) 3577–3594. [CrossRef] [MathSciNet] [Google Scholar]
  7. Y. Guan, H. Wang, Set-valued information systems, Information Sciences 176 (2006) 2507–2525. [CrossRef] [MathSciNet] [Google Scholar]
  8. Y.H. Qian, C.Y. Dang, J.Y. Liang, D.W. Tang, Set-valued ordered information systems, Information Sciences 179 (2009) 2809–2832. [CrossRef] [MathSciNet] [Google Scholar]
  9. Y. Leung, M.M. Fischer, W.Z. Wu, J.S. Mi, A rough set approach for the discovery of classification rules in interval-valued information systems, International Journal of Approximate Reasoning 47 (2008) 233–246. [Google Scholar]
  10. G.Y. Wang, Extension of rough set under incomplete information systems, in: Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, vol. 2, pp. 1098–1103. [Google Scholar]
  11. J. Grzymala-Busse, Characteristic relations for incomplete data: a generalization of the indiscernibility relation, in: S. Tsumoto, R. Slowinski, J. Komorowski, J. Grzymala-Busse (Eds.), Rough Sets and Current Trends in Computing, Lecture Notes in Computer Science, vol. 3066, Springer, Berlin/Heidelberg, 2004, pp. 244–253. [CrossRef] [Google Scholar]
  12. J.B. Zhang, T.R. Li, H.M. Chen, Composite rough sets for dynamic data mining, Information Sciences, 2014, 257(2):81–100. [CrossRef] [MathSciNet] [Google Scholar]
  13. J.B. Zhang, T.R. Li, H.M. Chen, Composite rough sets, in: J. Lei, F. Wang, H. Deng, D. Miao (Eds.), Artificial Intelligence and Computational Intelligence, Lecture Notes in Computer Science, vol. 7530, Springer, Berlin/ Heidelberg, 2012, pp. 150–159. [Google Scholar]
  14. H.M. Abu-Donia, Multi knowledge based rough approximations and applications, Knowledge-Based Systems 26 (2012) 20–29. [CrossRef] [Google Scholar]
  15. GE Hao, LI Longshu, YANG Chuanjian. Discernibility matrix-based reduct representation and quick algorithms [J]. Control and Decision, 2016, 31(1):12–20. [Google Scholar]
  16. Z. Pawlak, A. Skowron, Rough sets: some extensions, Information Sciences 177 (2007) 28–40. [Google Scholar]
  17. Zhang Wenxiu, Wu Weizhi, liang Jiye, The theory and method of rough set.Science Publishing Company. 2003.1. [Google Scholar]
  18. Y.H. Qian, J.Y. Liang, W. Pedrycz, C.Y. Dang, Positive approximation: an accelerator for attribute reduction in rough set theory, Artificial Intelligence 174 (2010) 597–618. [CrossRef] [MathSciNet] [Google Scholar]
  19. Z.Y. Xu, Z.P. Liu, B.R. Yang, W. Song, A quick attribute reduction algorithm with complexity of max(O(jCjjUj), O(jCj2jU/Cj)), Chinese Journal of Computers 29 (2006) 391–398. [Google Scholar]
  20. Y.H. Qian, J.Y. Liang, F. Wang, A positive-approximation based accelerated algorithm to feature selection from incomplete decision tables, Chinese Journal of Computers 34 (2011) 435–442. [CrossRef] [Google Scholar]
  21. J. Stefanowski, A. Tsoukias, Incomplete information tables and rough classification, Computational Intelligence 17 (2001) 545–566. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.