Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Fuzzy-Rough Instance Selection Richard Jensen Aberystwyth University, UK Chris Cornelis Ghent University, Belgium Richard Jensen and Chris Cornelis Outline • The importance of instance selection • Rough set theory • Fuzzy-rough sets • Fuzzy-rough instance selection • Experimentation • Conclusion Richard Jensen and Chris Cornelis Instance selection • Knowledge discovery • The problem of too much data • Requires storage • Intractable for data mining algorithms • Removing data that is noisy or irrelevant Richard Jensen and Chris Cornelis Rough set theory Upper Approximation Set A Lower Approximation Equivalence class Rx Rx is the set of all points that are indiscernible with point x 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 Richard Jensen and Chris Cornelis Fuzzy-rough sets • Parameterized relation • Fuzzy-rough definitions: Richard Jensen and Chris Cornelis Instance selection: basic idea Not needed Remove objects to keep the underlying approximations unchanged Richard Jensen and Chris Cornelis Instance selection: basic idea Remove objects to keep the underlying approximations unchanged Richard Jensen and Chris Cornelis FRIS-I Richard Jensen and Chris Cornelis FRIS-II Richard Jensen and Chris Cornelis FRIS-III Richard Jensen and Chris Cornelis Experimentation: setup Richard Jensen and Chris Cornelis Results: FRIS-I (heart) • (214 objects, 9 features) Richard Jensen and Chris Cornelis Results: FRIS-II (heart) Richard Jensen and Chris Cornelis Results: FRIS-III (heart) 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 Richard Jensen and Chris Cornelis • WEKA implementations of all fuzzy-rough methods can be downloaded from: Richard Jensen and Chris Cornelis