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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
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TKDE, 2004
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Outline
Motivation Objectives Feature Selection Approaches Review
Rough Fuzzy Rough
Conclusion Personal Comments
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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.
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Objectives
This review focuses on those recent techniques for feature selection that employ a rough-set-based methodology for this purpose.
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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
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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
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Rough Set Attribute Reduction
e=12,5e=0
e=2
0,4
31,6,7
QUICKREDUCT:
Variable precision rough sets
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Discernibility Matrix Approach
Removing those sets that are supersets of others
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Dynamic Reducts
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Experimental Results
RSAR < EBR<=SimRSAR<= AntRSAR<= GenRSARTime cost:
AntRSAR and SimRSAR outperform the other three methods.Performance:
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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
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Fuzzy Rough Attribute Reduction
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Rough Set-Based Feature Grouping
Selection Strategies:
•Individuals
•Grouping
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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.
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Personal Comments
Application Feature selection.
Advantage Fuzzy.
Drawback Fuzzy!
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