MR_IMQRA: An Efficient MapReduce Based Approach for Fuzzy Decision Reduct Computation

Fuzzy-rough set theory, an extension to classical rough set theory, is effectively used for attribute reduction in hybrid decision systems. However, it's applicability is restricted to smaller size datasets because of higher space and time complexities. In this work, an algorithm MR_IMQRA is developed as a MapReduce based distributed/parallel approach for standalone fuzzy-rough attribute reduction algorithm IMQRA. This algorithm uses a vertical partitioning technique to distribute the input data in the cluster environment of the MapReduce framework. Owing to the vertical partitioning, the proposed algorithm is scalable in attribute space and is relevant for scalable attribute reduction in the areas of Bioinformatics and document classification. This technique reduces the complexity of movement of data in shuffle and sort phase of MapReduce framework. A comparative and performance analysis is conducted on larger attribute space (high dimensional) hybrid decision systems. The comparative experimental results demonstrated that the proposed MR_IMQRA algorithm obtained good sizeup/speedup measures and induced classifiers achieving better classification accuracy.