Subscribe Now Subscribe Today
Research Article
 

Uncertainty Measures in Interval Ordered Information Systems



Lv Jinlai, Zhang Hui, Fan Wenlei and Du Xiaoping
 
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail
ABSTRACT

Interval information systems are generalized models of single-valued information systems which is an important formal framework for the development of data mining. In this study, in terms of introducing dominance relations, a rough set approach in interval ordered information systems is first established. Then, the concept of dominance entropy and dominance combination entropy in interval ordered information systems is come up with. Finally, through calculating of dominance entropy and dominance combination entropy in interval information systems based on dominance relations, it is proved that in the wake of enhancement of knowledge discernment, dominance entropy and dominance combination entropy increases monotonously. These results give a kind of feasible approaches to discover and acquisition of knowledge in interval ordered information systems.

Services
Related Articles in ASCI
Similar Articles in this Journal
Search in Google Scholar
View Citation
Report Citation

 
  How to cite this article:

Lv Jinlai, Zhang Hui, Fan Wenlei and Du Xiaoping, 2013. Uncertainty Measures in Interval Ordered Information Systems. Journal of Applied Sciences, 13: 3522-3527.

DOI: 10.3923/jas.2013.3522.3527

URL: https://scialert.net/abstract/?doi=jas.2013.3522.3527
 

REFERENCES
1:  Cheng, S.W., R.W. Dai, W.X. Xu and Y. Shi, 2006. Research on data mining and knowledge management and its applications in China's economic development: Significance and trend. Int. J. Inform. Technol. Decis. Making, 5: 585-596.
CrossRef  |  

2:  Shi, Y., Y. Peng, W.X. Xu and X.W. Tang, 2002. Data mining via multiple criteria linear programming: Applications in credit card portfolio management. Int. J. Inform. Technol. Decis. Making, 1: 131-151.
CrossRef  |  

3:  Greco, S., B. Matarazzo and R. Slowinski, 1998. A new rough set approach to multicriteria and multiattribute classification. Proceedings of the 1st International Conference on Rough Sets and Current Trends in Computing, June 22-26, 1998, Warsaw, Poland, pp: 60-67.

4:  Greco, S., B. Matarazzo and R. Slowinski, 2002. Rough sets methodology for sorting problems in presence of multiple attributes and criteria. Eur. J. Operat. Res., 8: 247-259.
CrossRef  |  Direct Link  |  

5:  Greco, S., B. Matarazzo, R. Slowinski and J. Stefanowski, 2005. An algorithm for induction of decision rules consistent with the dominance principle. Proceedings of the 2nd International Conference on Rough Sets and Current Trends in Computing, October 16-19, 2000, Banff, Canada, pp: 304-313.

6:  Dembczynski, K., R. Pindur and R. Susmaga, 2003. Generation of exhaustive set of rules within dominance-based rough set approach. Electron. Notes Theor. Comput. Sci., 82: 96-107.
CrossRef  |  

7:  Sai, Y., Y.Y. Yao and N. Zhong, 2001. Data analysis and mining in ordered information tables. Proceedings of the IEEE International Conference on Data Mining, November 29- December 2, 2001, San Jose, CA., USA., pp: 497-504.

8:  Shao, M.W. and W.X. Zhang, 2005. Dominance relation and rules in an incomplete ordered information system. Int. J. Intell. Syst., 20: 13-27.
CrossRef  |  

9:  Facchinetti, G., R.G. Ricci and S. Muzzioli, 1998. Note on ranking fuzzy triangular numbers. Int. J. Intell. Syst., 13: 613-622.
CrossRef  |  3.0.CO;2-N/abstract target='_blank' class='botlinks'>Direct Link  |  

10:  Xu, Z.S. and H.F. Gu, 2002. An approach to uncertain multi-attribute decision making with preference information on alternatives. Proceedings of the 9th Bellman Continuum International Workshop on Uncertain Systems and Soft Computing, July 24-27, 2002, Beijing, China, pp: 89-95.

11:  Xu, Z.S. and Q.L. Da, 2002. The uncertain OWA operator. Int. J. Intell. Syst., 17: 569-575.
CrossRef  |  

12:  Liang, J.Y. and K.S. Qu, 2001. Information measures of roughness of knowledge and rough sets in incomplete information systems. J. Syst. Sci. Syst. Eng., 24: 544-547.

13:  Liang, J.Y. and Z.B. Xu, 2002. The algorithm on knowledge reduction in incomplete information systems. Int. J. Uncertainty Fuzziness Knowl. Based Syst., 10: 95-103.
CrossRef  |  

14:  Liang, J., K.S. Chin, C. Dang and R.C. Yam, 2002. A new method for measuring uncertainty and fuzziness in rough set theory. Int. J. Gen. Syst., 31: 331-342.
CrossRef  |  

15:  Liang, J.Y. and Z.Z. Shi, 2004. The information entropy, rough entropy and knowledge granulation in rough set theory. Int. J. Uncertainty Fuzziness Knowl. Based Syst., 12: 37-46.
CrossRef  |  

16:  Mi, J.S., Y. Leung and W.Z. Wu, 2005. An uncertainty measure in partition-based fuzzy rough sets. Int. J. Gen. Syst., 34: 77-90.
CrossRef  |  

17:  Qian, Y. and J. Liang, 2006. Combination entropy and combination granulation in incomplete information system. Proceedings of the 1st International Conference on Rough Sets and Knowledge Technology, Volume 4062, July 24-26, 2006, Chongquing, China, pp: 184-190.

18:  Slezak, D., 2005. Association reducts: A framework for mining multi-attribute dependencies. Proceedings of the 15th International Symposium on Foundations of Intelligent Systems, Volume 3488, May 25-28, 2005, Saratoga Springs, NY., USA., pp: 354-363.

19:  Slezak, D., 2002. Approximate entropy reducts. Fund. Inform., 53: 365-390.
Direct Link  |  

20:  Malyszko, D. and J. Stepaniuk, 2008. Standard and fuzzy rough entropy clustering algorithms in image segmentation. Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing, Volume 5306, October 23-25, 2008, Akron, OH., USA., pp: 409-418.

21:  Malyszko, D. and J. Stepaniuk, 2010. Adaptive rough entropy clustering algorithms in image segmentation. Fund. Informaticae, 98: 199-231.
CrossRef  |  

22:  Malyszko, D. and J. Stepaniuk, 2010. Adaptive multilevel rough entropy evolutionary thresholding. Inform. Sci., 180: 1138-1158.
CrossRef  |  

23:  Pawlak, Z. and A. Skowron, 2007. Rudiments of rough sets. Inform. Sci., 177: 3-27.
CrossRef  |  Direct Link  |  

24:  Hu, Q.H., D, Yu, J.F. Liu and C.X. Wu, 2008. Neighborhood rough set based heterogeneous feature subset selection. Inform. Sci., 178: 3577-3594.
Direct Link  |  

25:  Hu, Q., Z. Xie and D. Yu, 2007. Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation. Pattern Recognit., 40: 3509-3521.
CrossRef  |  

26:  Yao, Y. and Y. Zhao, 2008. Attribute reduction in decision-theoretic rough set models. Inform. Sci., 178: 3356-3373.
CrossRef  |  Direct Link  |  

27:  Yao, Y.Y., 2007. Decision-Theoretic Rough Set Models. In: Rough Sets and Knowledge Technology, Yao, J.T., P. Lingras, W.Z. Wu, M. Szczuka, N.J. Cercone and D. Slezak (Eds.). Springer, Berlin Heidelberg, Germany, pp: 1-12.

28:  Wei, W., J.Y. Liang, Y.H. Qian, F. Wang and C.Y. Dang, 2010. Comparative study of decision performance of decision tables induced by attribute reductions. Int. J. Gen. Syst., 39: 813-838.
CrossRef  |  Direct Link  |  

29:  Zhang, J.B., T.R. Li and D. Liu, 2010. An approach for incremental updating approximations in Variable precision rough sets while attribute generalized. Proceedings of the 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, November 15-16, 2010, Hangzhou, China, pp: 77-81.

30:  Yang, X.B., M. Zhang, H.L. Dou and J.Y. Yang, 2011. Neighborhood systems-based rough sets in incomplete information system. Knowledge-Based Syst., 24: 858-867.
CrossRef  |  Direct Link  |  

31:  Greco, S., B. Matarazzo and R. Slowinski, 2001. Rough sets theory for multicriteria decision analysis. Eur. J. Oper. Res., 129: 1-47.
CrossRef  |  Direct Link  |  

©  2021 Science Alert. All Rights Reserved