Fuzzy Rough Discernibility Matrix Based Feature Subset Selection with MapReduce

Abstract

Fuzzy-rough set theory (FRST) is a hybridization of fuzzy sets with rough sets with applications to attribute reduction in hybrid decision systems. The existing reduct computation approaches in fuzzy-rough sets are not scalable to large scale decision systems owing to higher space complexity requirements. Iterative MapReduce framework of Apache Spark facilitates the development of scalable distributed algorithms with fault tolerance. This work introduces algorithm MR-FRDM-SBE as one of the first attempts towards scalable fuzzy-rough set based attribute reduction. MR-FRDM-SBE algorithm is a combination of a novel incremental approach for the construction of distributed fuzzy-rough discernibility matrix and Sequential Backward Elimination control strategy based distributed fuzzy-rough attribute reduction using a discernibility matrix. A comparative experimental study conducted using large scale benchmark hybrid decision systems demonstrated the relevance of the proposed approach in scalable attribute reduction and better classification model construction.