A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Type-2 Fuzzy Logic: Theory and Applications
Type-2 Fuzzy Logic: Theory and Applications
New approaches to fuzzy-rough feature selection
IEEE Transactions on Fuzzy Systems
Advances and challenges in interval-valued fuzzy logic
Fuzzy Sets and Systems
Fuzzy-rough data reduction with ant colony optimization
Fuzzy Sets and Systems
Feature selection with fuzzy decision reducts
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Interval type-2 fuzzy logic systems: theory and design
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Fuzzy-rough nearest neighbour classification and prediction
Theoretical Computer Science
Approximations and uncertainty measures in incomplete information systems
Information Sciences: an International Journal
On characterization of generalized interval type-2 fuzzy rough sets
Information Sciences: an International Journal
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One of the many successful applications of rough set theory has been to the area of feature selection. The rough set principle of using only the supplied data and no other information has many benefits, where most other methods require supplementary knowledge. Fuzzy-rough set theory has recently been proposed as an extension of this, in order to better handle the uncertainty present in real data. However, following this approach, there has been no investigation (theoretical or otherwise) into how to deal with missing values effectively, another problem encountered when using real world data. This paper proposes an extension of the fuzzy-rough feature selection methodology, based on interval-valued fuzzy sets, as a means to counter this problem via the representation of missing values in an intuitive way.