Some Investigations on Higher Mathematics Scores for Chinese University Student
To investigate some relations between higher mathe¬matics scores in Chinese graduate student entrance examination and calculus (resp. linear algebra, probability statistics) scores in subject's completion examination of Chinese university, we select 20 students as a sample, take higher mathematics score as a decision attribute and take calculus score, linear algebra score, probability statistics score as condition attributes. In this paper, we are based on rough-set theory (Rough-set theory is a logic-mathematical method proposed by Z. Pawlak. In recent years, this theory has been widely implemented in the many fields of natural science and societal science.) to investigate importance of condition attributes with respective to decision attribute and strength of condition attributes supporting decision attribute. Results of this investigation will be helpful for university students to raise higher mathematics scores in Chinese graduate student entrance examination.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081882Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 777
 G. Alvatore, M. Bentto and S. Roman, Rough set theory for multi criteria decision analysis, European Journal of Operational Research, 129(2001), 1-47.
 Y. Cheng and Y. Ge, Influencing factors with respect to basic essence in national defence for chinese university students, 2008 International Sym¬posium on Computer Science and Computational Technology, 2(2008), 528-531.
 C. Donna, Artificial interagency research in Japan, Artificial Intelligence, 91(1997), 122-129.
 A. Erbert, Scientific discovery and simplicity of method, Artificial Intel-ligence, 91(1997), 177-181.
 Y. Ge, Granularity-wise separation in covering approximation spaces, 2008 IEEE International Conference on Granular Computing, 238-243.
 R. Golan and W. Ziarko, Methodology for stock market analysis utilizing rough set theory, Proc. of IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, New Jersey, 22(1995), 32-40.
 Z. Pawlak, Rough sets, International Journal of Computer and Information Sciences, 11(1982), 341-356.
 Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning About Data, Boston: Kluwer Academic Publishers, 1991.
 Z. Pawlak, Rough sets, Communications of ACM, 38(1995), 89-95.
 Z. Pawlak, Vagueness and uncertainty - a rough set perspective, Com-putational Intelligence, 11(1995), 227-232.
 S. Padmini and H. Donald, Vocabulary mining for information retrieval: rough sets and fuzzy sets, Information Processing and Management, 37(2002), 15-38.
 K. Qin, Y. Gao and Z. Pei, On covering rough sets, in RSKT 2007, LNAI, 4481(2007), 34-41.
 P. Roger, The Emperor's New Mind: Concerning Computer's Minds and The Laws of Physics, Oxford University Press, 1989, 65-70.
 M. Stiefld and S. Smoliar, What computers still can't do: five reviews and a response, Artificial Intelligence, 81(1996), 95-97.
 S. Tsumoto, Automated discovery of medical expert system rules from clinical databases based on rough set, Proc. of Second International Conf. on Knowledge discovery and data Mining, USA. 32(1996), 63-72.
 M. Yahia, R. Mahmodr and N. Sulaimann, Rough neural expert systems, Expert system with Applications, 18(2002), 87-99.
 S. Yang, J. Qian and Y. Ge, Uncertain decision analysis in stocks prices fluctuations, J. of Suzhou University, 24(2008), 6-10.
 W. Zhu and F. Wang, On Three types of covering rough sets, IEEE Transactions on Knowledge and Data Engineering, 19(2007), 1131-1144.