Scalable IQRA_IG algorithm: An iterative mapreduce approach for reduct computation

Abstract

Feature Selection is an important preprocessing step in any machine learning model construction. Rough Set based feature selection (Reduct) methods provide efficient selection of attributes for the model without loss of information. Quick Reduct Algorithm is a key Reduct computation approach in Complete Symbolic Decision Systems. Authors have earlier implemented a scalable approach for Quick Reduct Algorithm as In-place MapReduce based Quick Reduct Algorithm using Twister’s Iterative MapReduce Framework. Improved Quick Reduct Algorithm is a standalone extension to Quick Reduct Algorithm by incorporating Trivial Ambiguity Resolution and Positive Region Removal. This work develops design and implementation of distributed/parallel algorithm for Improved Quick Reduct Algorithm by incorporation of Trivial Ambiguity Resolution and Positive Region Removal in In-place MapReduce based Quick Reduct Algorithm. Experiments conducted on large benchmark decision systems have empirically established the significance of computational gain and scalability of proposed algorithm in comparison to earlier approaches in literature.