A Review of Global Sensitivity Analysis Methods and a comparative case study on Digit Classification
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913403147124736 |
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| author | Sadeghi, Zahra Matwin, Stan |
| author_facet | Sadeghi, Zahra Matwin, Stan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16975 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Review of Global Sensitivity Analysis Methods and a comparative case study on Digit Classification Sadeghi, Zahra Matwin, Stan Machine Learning Artificial Intelligence 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. |
| title | A Review of Global Sensitivity Analysis Methods and a comparative case study on Digit Classification |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2406.16975 |