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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