A Review of Global Sensitivity Analysis Methods and a comparative case study on Digit Classification
Machine Learning
2024-06-26 v1 Artificial Intelligence
Abstract
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.
Cite
@article{arxiv.2406.16975,
title = {A Review of Global Sensitivity Analysis Methods and a comparative case study on Digit Classification},
author = {Zahra Sadeghi and Stan Matwin},
journal= {arXiv preprint arXiv:2406.16975},
year = {2024}
}