A Deep Neural Network Based Approach to Building Budget-Constrained Models for Big Data Analysis

TL;DR

This paper presents a method using deep neural networks to select important features and build budget-constrained models for big data analysis, reducing data collection costs while maintaining model accuracy.

Contribution

It introduces a novel approach to eliminate less important features in DNNs to develop cost-effective models within specified budgets.

Findings

Feasible feature elimination for budget constraints

Supports user-defined budget in model development

Effective in big data scenarios

Abstract

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.