A Review of Global Sensitivity Analysis Methods and a comparative case study on Digit Classification
<|reference_start|>A Review of Global Sensitivity Analysis Methods and a comparative case study on Digit Classification: 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.<|reference_end|>
@article{sadeghi2024a,
    title={A Review of Global Sensitivity Analysis Methods and a comparative case
  study on Digit Classification},
    author={Zahra Sadeghi, Stan Matwin},
    journal={arXiv preprint arXiv:2406.16975},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16975},
    primaryClass={cs.LG cs.AI}
}