  This paper presents an approach for automation of interpretable feature
selection for Internet Of Things Analytics (IoTA) using machine learning (ML)
techniques. Authors have conducted a survey over different people involved in
different IoTA based application development tasks. The survey reveals that
feature selection is the most time consuming and niche skill demanding part of
the entire workflow. This paper shows how feature selection is successfully
automated without sacrificing the decision making accuracy and thereby reducing
the project completion time and cost of hiring expensive resources. Several
pattern recognition principles and state of art (SoA) ML techniques are
followed to design the overall approach for the proposed automation. Three data
sets are considered to establish the proof-of-concept. Experimental results
show that the proposed automation is able to reduce the time for feature
selection to $2$ days instead of $4-6$ months which would have been required in
absence of the automation. This reduction in time is achieved without any
sacrifice in the accuracy of the decision making process. Proposed method is
also compared against Multi Layer Perceptron (MLP) model as most of the state
of the art works on IoTA uses MLP based Deep Learning. Moreover the feature
selection method is compared against SoA feature reduction technique namely
Principal Component Analysis (PCA) and its variants. The results obtained show
that the proposed method is effective.
