Title: Interpretable Feature Recommendation for Signal Analytics,
Abstract: This paper presents an automated approach for interpretable feature
recommendation for solving signal data analytics problems. The method has been
tested by performing experiments on datasets in the domain of prognostics where
interpretation of features is considered very important. The proposed approach
is based on Wide Learning architecture and provides means for interpretation of
the recommended features. It is to be noted that such an interpretation is not
available with feature learning approaches like Deep Learning (such as
Convolutional Neural Network) or feature transformation approaches like
Principal Component Analysis. Results show that the feature recommendation and
interpretation techniques are quite effective for the problems at hand in terms
of performance and drastic reduction in time to develop a solution. It is
further shown by an example, how this human-in-loop interpretation system can
be used as a prescriptive system.