An Applications of Information Systems on Macro-Economic Climate Index of China
Mei He, Xun Ge
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DOI: 10.4236/me.2011.23047   PDF    HTML     5,872 Downloads   9,028 Views  

Abstract

Recently, National Bureau of Statistics of China has released macro-economic climate index of China from 2009-02 to 2010-05.Based on these indices, we establish an information system.In this information system, monitoring signal is taken as a decision attribute and coincident index, leading index, lagging index are taken as condition attributes.We use rough-set theory to investigate the importance of each condition attribute with respective to decision attribute and the strength of each condition attribute supporting decision attribute.Results of this investigation will be helpful for Chinese government to make active macro-economic policy and to maintain the steady and relatively fast development of Chinese economy.

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M. He and X. Ge, "An Applications of Information Systems on Macro-Economic Climate Index of China," Modern Economy, Vol. 2 No. 3, 2011, pp. 421-426. doi: 10.4236/me.2011.23047.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] National Bureau of Statistics of China, “Macro-Economic Climate Index of China from 2009-02 to 2010-05,” May 2010. http://www.stats.gov.cn/english/
[2] G. Alvatore, M. Bentto and S. Roman, “Rough Set Theory for Multi Criteria Decision Analysis,” European Journal of Operational Research, Vol. 129, No. 1, 2001, pp. 1-47. doi:10.1016/S0377-2217(00)00167-3
[3] C. Donna, “Artificial Interagency Research in Japan,” Artificial Intelligence, Vol. 91, No. 1, 1997, pp. 122-129.
[4] A. Erbert, “Scientific Discovery and Simplicity of Method,” Artificial Intelligence, Vol. 91, No. 2, 1997, pp. 177-181. doi:10.1016/S0004-3702(97)00019-2
[5] X. Ge and J. Qian, “Some Investigations on Higher Mathematics Scores for Chinese University Students,” International Journal of Computer and Information Engineering, Vol. 3, No. 4, 2009, pp. 209-212.
[6] X. Ge, J. Li and Y. Ge, “Some Separations in Covering Approximation Spaces,” International Journal of Computational and Mathematical Sciences, Vol. 4, No. 3, 2010, pp. 156-160.
[7] Z. Pawlak, “Rough Sets,” International Journal of Computer and Information Sciences, Vol. 11, No. 5, 1982, pp. 341-356. doi:10.1007/BF01001956
[8] Z. Pawlak, “Rough Sets: Theoretical Aspects of Reasoning about Data,” Kluwer Academic Publishers, Norwell, 1991.
[9] Z. Pawlak, “Rough Sets,” Communications of ACM, Vol. 38, No. 11, 1995, pp. 89-95. doi:10.1145/219717.219791
[10] Z. Pawlak, “Vagueness and Uncertainty: A Rough Set Perspective,” Computational Intelligence, Vol. 11, No. 2, 1995, pp. 227-232. doi:10.1111/j.1467-8640.1995.tb00029.x
[11] S. Padmini and H. Donald, “Vocabulary Mining for Information Retrieval:Rough Sets and Fuzzy Sets,” Information Processing and Management, Vol. 37, No. 1, 2002, pp. 15-38.
[12] K. Qin, Y. Gao and Z. Pei, “On Covering Rough Sets,” Lecture Notes in Artificial Intelligence, Vol. 4481, 2007, pp. 34-41.
[13] M. Stiefki and S. Smoliar, “What Computers Still Can’T Do: Five Reviews and a Response,” Artificial Intelligence, Vol. 80, No. 1, 1996, pp. 95-97. doi:10.1016/0004-3702(95)00082-8
[14] M. Yahia, R. Mahmodr and N. Sulaimann, “Rough Neural Expert Systems,” Expert System with Applications, Vol. 18, No. 2, 2002, pp. 87-99. doi:10.1016/S0957-4174(99)00055-X
[15] W. Zhang, W. Wu, J. Liang and D. Li, “Rough-Set Theory and Its Method,” Chinese Science Press, Beijing, 2002.
[16] W. Zhu and F. Wang, “On Three Types of Covering Rough Sets,” IEEE Transactions on Knowledge and Data Engineering, Vol. 19, No. 8, 2007, pp. 1131-1144. doi:10.1109/TKDE.2007.1044

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