Hybrid fuzzy-rough rule induction and feature selection

Richard Jensen, Chris Cornelis, Qiang Shen
2009 2009 IEEE International Conference on Fuzzy Systems  
The automated generation of feature patternbased if-then rules is essential to the success of many intelligent pattern classifiers, especially when their inference results are expected to be directly human-comprehensible. Fuzzy and rough set theory have been applied with much success to this area as well as to feature selection. Since both applications of rough set theory involve the processing of equivalence classes for their successful operation, it is natural to combine them into a single
more » ... egrated method that generates concise, meaningful and accurate rules. This paper proposes such an approach, based on fuzzy-rough sets. The algorithm is experimentally evaluated against leading classifiers, including fuzzy and rough rule inducers, and shown to be effective.
doi:10.1109/fuzzy.2009.5277058 dblp:conf/fuzzIEEE/JensenCS09 fatcat:i6263vsq2ve5bnbswgvuvaa5oy