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Semantics-preserving dimensionality reduction rough and fuzzy rough based approaches

Semantics-preserving dimensionality reduction rough and fuzzy rough based approaches. Author :Richard Jensen and Qiang Shen Reporter : Tse Ho Lin 2008/5/20. TKDE, 2004. Outline. Motivation Objectives Feature Selection Approaches Review Rough Fuzzy Rough Conclusion Personal Comments.

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Semantics-preserving dimensionality reduction rough and fuzzy rough based approaches

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  1. Semantics-preserving dimensionality reduction rough and fuzzy rough based approaches Author :Richard Jensen and Qiang Shen Reporter : Tse Ho Lin 2008/5/20 TKDE, 2004

  2. Outline • Motivation • Objectives • Feature Selection Approaches Review • Rough • Fuzzy Rough • Conclusion • Personal Comments

  3. 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.

  4. Objectives • This review focuses on those recent techniques for feature selection that employ a rough-set-based methodology for this purpose.

  5. 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. 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

  7. Rough Set Attribute Reduction e=0 e=1 0,4 2,5 e=2 1,6,7 3 Variable precision rough sets QUICKREDUCT:

  8. Discernibility Matrix Approach Removing those sets that are supersets of others

  9. Dynamic Reducts

  10. Experimental Results Time cost: RSAR < EBR<=SimRSAR<= AntRSAR<= GenRSAR Performance: AntRSAR and SimRSAR outperform the other three methods.

  11. 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

  12. Fuzzy Rough Attribute Reduction

  13. Rough Set-Based Feature Grouping • Selection Strategies: • Individuals • Grouping

  14. 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.

  15. Personal Comments • Application • Feature selection. • Advantage • Fuzzy. • Drawback • Fuzzy!

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