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Richard Jensen and Chris Cornelis Chris Cornelis Chris Cornelis Ghent University, Belgium Richard Jensen Richard Jensen Aberystwyth University, UK Fuzzy-Rough.

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Presentation on theme: "Richard Jensen and Chris Cornelis Chris Cornelis Chris Cornelis Ghent University, Belgium Richard Jensen Richard Jensen Aberystwyth University, UK Fuzzy-Rough."— Presentation transcript:

1 Richard Jensen and Chris Cornelis Chris Cornelis Chris Cornelis Ghent University, Belgium Richard Jensen Richard Jensen Aberystwyth University, UK Fuzzy-Rough Instance Selection

2 Richard Jensen and Chris Cornelis Outline The importance of instance selection Rough set theory Fuzzy-rough sets Fuzzy-rough instance selection Experimentation Conclusion

3 Richard Jensen and Chris Cornelis Knowledge discovery The problem of too much data Requires storage Intractable for data mining algorithms Removing data that is noisy or irrelevant Instance selection

4 Richard Jensen and Chris Cornelis Rough set theory Rx is the set of all points that are indiscernible with point x Upper Approximation Set A Lower Approximation Equivalence class Rx

5 Richard Jensen and Chris Cornelis Fuzzy-rough sets Approximate equality Handle real-valued features via fuzzy tolerance relations instead of crisp equivalence Better noise and uncertainty handling Focus has been on feature selection, not instance selection

6 Richard Jensen and Chris Cornelis Fuzzy-rough sets Parameterized relation Fuzzy-rough definitions:

7 Richard Jensen and Chris Cornelis Instance selection: basic idea Not needed Remove objects to keep the underlying approximations unchanged

8 Richard Jensen and Chris Cornelis Instance selection: basic idea Remove objects to keep the underlying approximations unchanged

9 Richard Jensen and Chris Cornelis FRIS-I

10 Richard Jensen and Chris Cornelis FRIS-II

11 Richard Jensen and Chris Cornelis FRIS-III

12 Richard Jensen and Chris Cornelis Experimentation: setup

13 Richard Jensen and Chris Cornelis Results: FRIS-I (heart) (214 objects, 9 features)

14 Richard Jensen and Chris Cornelis Results: FRIS-II (heart)

15 Richard Jensen and Chris Cornelis Results: FRIS-III (heart)

16 Richard Jensen and Chris Cornelis Conclusion Proposed new techniques for instance selection based on fuzzy-rough sets Managed to reduce the number of instances significantly, retaining classification accuracy Future work Many possibilities for novel fuzzy-rough instance selection methods Comparisons with non-rough techniques Improving the complexity of FRIS-III Combined instance/feature selection

17 Richard Jensen and Chris Cornelis WEKA implementations of all fuzzy-rough methods can be downloaded from:

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