1. 國立雲林科技大學 National Yunlin University of Science and Technology Semantics-preserving dimensionality reduction rough and fuzzy rough based approaches Author :Richard Jensen and Qiang Shen Reporter : Tse Ho Lin 2008/5/20 1 TKDE, 2004

2. N.Y.U.S.T. I. M. Outline Motivation Objectives Feature Selection Approaches Review  Rough  Fuzzy Rough Conclusion Personal Comments 2

3. N.Y.U.S.T. I. M. Motivation Conventional rough set theory are unable to deal with real-valued attributes effectively. What’s current trends and future directions for rough-set-based methodologies. 3

4. N.Y.U.S.T. I. M. Objectives This review focuses on those recent techniques for feature selection that employ a rough-set-based methodology for this purpose. 4

5. N.Y.U.S.T. I. M. Feature Selection Review Rough  Rough Set Attribute Reduction  Discernibility Matrix Approach  Dynamic Reducts  Experimental Results Fuzzy Rough  Fuzzy Rough Attribute Reduction  Rough Set-Based Feature Grouping 5

6. N.Y.U.S.T. I. M. Feature Selection Review Rough  Rough Set Attribute Reduction  Discernibility Matrix Approach  Dynamic Reducts  Experimental Results Fuzzy Rough  Fuzzy Rough Attribute Reduction  Rough Set-Based Feature Grouping 6

7. N.Y.U.S.T. I. M. Rough Set Attribute Reduction e=1 2,5 e=0 e=2 0,4 3 1,6,7 QUICKREDUCT: Variable precision rough sets 7

8. N.Y.U.S.T. I. M. Discernibility Matrix Approach Removing those sets that are supersets of others 8

9. N.Y.U.S.T. I. M. Dynamic Reducts 9

10. N.Y.U.S.T. I. M. Experimental Results RSAR < EBR<=SimRSAR<= AntRSAR<= GenRSARTime cost: AntRSAR and SimRSAR outperform the other three methods.Performance: 10

11. N.Y.U.S.T. I. M. Feature Selection Review Rough  Rough Set Attribute Reduction  Discernibility Matrix Approach  Dynamic Reducts  Experimental Results Fuzzy Rough  Fuzzy Rough Attribute Reduction  Rough Set-Based Feature Grouping 11

12. N.Y.U.S.T. I. M. Fuzzy Rough Attribute Reduction 12

13. N.Y.U.S.T. I. M. Rough Set-Based Feature Grouping Selection Strategies : Individuals Grouping 13

14. N.Y.U.S.T. I. M. Conclusion This prompted research into the use of fuzzy- rough sets for feature selection. Additionally, the new direction in feature selection, feature grouping, was highlighted. 14

15. N.Y.U.S.T. I. M. Personal Comments Application  Feature selection. Advantage  Fuzzy. Drawback  Fuzzy! 15