Scalable mRMR feature selection to handle high dimensional datasets: Vertical partitioning based Iterative MapReduce framework
While building machine learning models, Feature selection (FS) stands out as
an essential preprocessing step used to handle the uncertainty and vagueness in
the data. Recently, the minimum Redundancy and Maximum Relevance (mRMR)
approach has proven to be effective in obtaining the irredundant feature
subset. Owing to the generation of voluminous datasets, it is essential to
design scalable solutions using distributed/parallel paradigms. MapReduce
solutions are proven to be one of the best approaches to designing
fault-tolerant and scalable solutions. This work analyses the existing
MapReduce approaches for mRMR feature selection and identifies the limitations
thereof. In the current study, we proposed VMR_mRMR, an efficient vertical
partitioning-based approach using a memorization approach, thereby overcoming
the extant approaches limitations. The experiment analysis says that VMR_mRMR
significantly outperformed extant approaches and achieved a better
computational gain (C.G). In addition, we also conducted a comparative analysis
with the horizontal partitioning approach HMR_mRMR [1] to assess the strengths
and limitations of the proposed approach.