A Review of Global Sensitivity Analysis Methods and a comparative case study on Digit Classification
This work addresses the need for effective sensitivity analysis in high-dimensional data processing, but it is incremental as it focuses on reviewing and comparing existing methods.
The paper provides a comprehensive review and comparison of global sensitivity analysis methods, proposing a methodology to evaluate their efficacy through a case study on the MNIST digit dataset.
Global sensitivity analysis (GSA) aims to detect influential input factors that lead a model to arrive at a certain decision and is a significant approach for mitigating the computational burden of processing high dimensional data. In this paper, we provide a comprehensive review and a comparison on global sensitivity analysis methods. Additionally, we propose a methodology for evaluating the efficacy of these methods by conducting a case study on MNIST digit dataset. Our study goes through the underlying mechanism of widely used GSA methods and highlights their efficacy through a comprehensive methodology.