A Support Vector Machines Method for Classification Problem 10. ) Times New Roman, Conclusion References Acknowledgment

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1 CSO 2010 (IEEE CS ) 0. IEEE CS IEEE CS (Title): CSO (14-point) (Time New Roman, boldface type) ( ) ( ) A Support Vector Machines Method for Classification Problem 10 Study on Research on 2 3 ( word -- ) Times New Roman, Conclusion References Acknowledgment 2. : CSO Zhang San San Z. (a) 2 Sanfeng Zhang, Sihai Li, Wuyi Wang Department of Computer Sciences, (b) 2 Sanfeng Zhang Liuxing Zhao Qiutian Qian xzhao@126.com

2 (c) Sanfeng Zhang, Sihai Li Liuxing Zhao Qiutian Qian, L. Zadeh (d) Sanfeng Zhang Liuxing Zhao Liuxing Zhao lxzhao@126.com lxzhao@126.com Qiutian Qian 3. (Abstract): IEEE 200 A B C D E EI ISTP 2-3 Variable precision rough set for multiple decision attribute analysis : [Purpose] In order to solve the multi-attribute decision analysis (MADA) problem with multiple conflicting decision attributes and multiple condition attributes, a variable precision rough set (VPRS) model is used in this paper. [Method] By introducing confidence measure and β-reduct, the VPRS model can rationally solve the conflicting decision analysis problem with multiple decision attributes and multiple condition attributes. For illustration, a medical diagnosis example is utilized to show the feasibility of the VPRS model in solving MADA problem with

3 multiple decision attributes and multiple condition attributes. [Results] Empirical results show that the decision rule with highest confidence measure will be used as the final decision rules in the MADA problem with multiple conflicting decision attributes and multiple condition attributes if there are some conflicts among decision rules resulting from multiple decision attributes. [Conclusions] The confidence-measure-based VPRS model can effectively solve the conflicts of decision rules from multiple decision attributes and thus a class of MADA problem with multiple conflicting decision attributes and multiple condition attributes are solved. [ ] [ ] β- [ ] β- [ ] IEEE (Introduction): Introduction Section II, Section III Section IV 5. : CSO cm 2 (Justify) (align left) (align right) (center) (1), (2),, (20) (2.1), (3.2), (3.5) 6. (Heading 1): 10

4 I, II, III, IV, (. ) I. INTRODUCTION II. MAIN METHODOLOGY. 7. (Heading 2): 10 A, B, C, D (. ) A. Basic Principle 8. IEEE 10 1) For author/s of only one affiliation (Heading 3): To change the default, adjust the template as follows TABLE I. DATABASE CONTEXTS, Figure 1. Some results 2 TABLE I. DATABASE CONTEXTS A B C 10. : IEEE 8 ACKNOWLEDGMENT 11. (Conclusions) 12. (Acknowledgment):

5 Acknowledgment 13. (References): 8 9 (9pt) in this approach, for example [1]. We use References Example: [1] G. Eason, B. Noble, and I. N. Sneddon, On certain integrals of Lipschitz-Hankel type involving products of Bessel functions, Phil. Trans. Roy. Soc. London, vol. A247, pp , [2] M. Young, The Technical Writer s Handbook. Mill Valley, CA: University Science, [3] H. Goto, Y. Hasegawa, and M. Tanaka, Efficient Scheduling Focusing on the Duality of MPL Representatives, Proc. IEEE Symp. Computational Intelligence in Scheduling (SCIS 07), IEEE Press, Dec. 2007, pp S.F. Zhang [J][M][C] IEEE 14. : CSO2010 Latex Word 15. ( ) cso2010org@gmail.com., 15

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