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While building machine learning models, Feature selection (FS) stands out asan essential preprocessing step used to handle the uncertainty and vagueness inthe data. Recently, the minimum Redundancy and Maximum Relevance (mRMR)approach has proven to be effective in obtaining the irredundant featuresubset. Owing to the generation of voluminous datasets, it is essential todesign scalable solutions using distributed/parallel paradigms. MapReducesolutions are proven to be one of the best approaches to designingfault-tolerant and scalable solutions. This work analyses the existingMapReduce approaches for mRMR feature selection and identifies the limitationsthereof. In the current study, we proposed VMR_mRMR, an efficient verticalpartitioning-based approach using a memorization approach, thereby overcomingthe extant approaches limitations. The experiment analysis says that VMR_mRMRsignificantly outperformed extant approaches and achieved a bettercomputational gain (C.G). In addition, we also conducted a comparative analysiswith the horizontal partitioning approach HMR_mRMR [1] to assess the strengthsand limitations of the proposed approach.

Scalable mRMR feature selection to handle high dimensional datasets: Vertical partitioning based Iterative MapReduce framework