A Deep Neural Network Based Approach to Building Budget-Constrained Models for Big Data Analysis
This addresses cost reduction for big data analysis, but it is incremental as it builds on existing deep neural network methods.
The paper tackles the problem of reducing data collection costs in deep learning by selecting a subset of features to build budget-constrained models, showing that the approach is feasible and supports user selection within a given budget.
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.