  Deep learning approaches require collection of data on many different input
features or variables for accurate model training and prediction. Since data
collection on input features could be costly, it is crucial to reduce the cost
by selecting a subset of features and developing a budget-constrained model
(BCM). In this paper, we introduce an approach to eliminating less important
features for big data analysis using Deep Neural Networks (DNNs). Once a DNN
model has been developed, we identify the weak links and weak neurons, and
remove some input features to bring the model cost within a given budget. The
experimental results show our approach is feasible and supports user selection
of a suitable BCM within a given budget.
