Making best use of model evaluations to compute sensitivity indices
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Sensitivity analysis aims at assessing the relative importance of the different features used to build a classification model.
“Sensitivity analysis, instead, aims at assessing the relative importance of the different features used to build a classification model [48].”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [48]
The Introduction (page unavailable) says global sensitivity analysis quantifies the relative importance of model input variables in determining an output. That supports the general purpose described, but the paper does not specifically address features used to build a classification model. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe most commonly used method for total sensitivity indices is Sobol (1993), in a computationally improved form given by Saltelli (2002).
“According to the authors, the most commonly used method is the one by Sobol (1993), in a computationally improved form given by Saltelli (2002).”
From On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness (2019) · cites this paper as Saltelli 2002
The Introduction (page unavailable) identifies Sobol’s sensitivity-index procedure and says this paper introduces an extended version that outperforms FAST. The Conclusions (page unavailable) report an approximately 50% reduction in model-evaluation cost. Our copy does not establish that Sobol’s method was the most commonly used method for total sensitivity indices. (LLM, conf=0.84)
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Cites
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Sensitivity Anaysis as an Ingredient of Modeling
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Self-citationSensitivity Analysis as an Ingredient of Modeling
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Self-citationAbout the use of rank transformation in sensitivity analysis of model output
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Sensitivity analysis of model output
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Flagged first, then by citations
Research works printed but not linked: 5
- F. Campolongo, A. Saltelli, in: A. Saltelli, K. Chan, M. Scott (Eds.), Design of experiment, Sensitivity Analysis, Probability and Statistics Series, John Wiley, 2000, pp. 51-63.
- M.D. McKay, Variance-based methods for assessing uncertainty importance in NUREG-1150 analyses, LA-UR-96-2695, 1996, pp. 1-27.
- H. Rabitz, Ö.F. Aliş, in: A. Saltelli et al. (Eds.), Managing the Tyranny of Parameters in Mathematical Modelling of Physical Systems, 2000.
- A. Saltelli, K. Chan, M. Scott (Eds.), Sensitivity Analysis, Probability and Statistics Series, John Wiley, 2000.
- S. Tarantola, A. Corradi, P. Ruffo, A. Saltelli, Global sensitivity analysis techniques for the analysis of the oil potential of sedimentary basins, in: P. Prado, R. Bolado (Eds.), 3rd International Symposium on Sensitivity Analysis of Model Output, Proceedings of SAMO 2001, CIEMAT Publication, Madrid, 2001.
Cited by
Sorted by citations
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Explainable AI: a review of machine learning interpretability methods
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“According to the authors, the most commonly used method is the one by Sobol (1993), in a computationally improved form given by Saltelli (2002).”
MethodFame for sale: Efficient detection of fake Twitter followers
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“Sensitivity analysis, instead, aims at assessing the relative importance of the different features used to build a classification model [48].”
BackgroundRatings and Rankings: Voodoo or Science?
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Self-citationAn economic, sustainability, and energetic model of biodiesel production from microalgae
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Incorporating Artificial Intelligence into Healthcare Workflows: Models and Insights
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- Authors
- Saltelli, A.
- Affiliations
- European Commission (BE)
- Corresponding author
- Saltelli, Andrea
- ORCID
- Saltelli, Andrea
- Venue
- Computer Physics Communications
- Publisher
- Elsevier BV
- Published
- 2002-05
- Volume
- 145
- Issue
- 2
- Pages
- 280-297
- ISSN
- 0010-4655 (ISSN-L)
- OpenAlex
- W2029767409
- Cited by
- 2,228
- References
- 18
- Citations by year
- 2026: 202 · 2025: 184 · 2024: 181 · 2023: 203 · 2022: 179 · 2021: 195 · 2020: 133 · 2019: 122 · 2018: 115 · 2017: 107 · 2016: 81 · 2015: 96 · 2014: 81 · 2013: 69 · 2012: 70
- FWCI
- 10.69
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Licence
- Locations
- Computer Physics Communications (publishedVersion)
- HAL (submittedVersion, cc-by-nc)
- Joint Research Centre (European Commission) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Probabilistic and Robust Engineering Design
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Physical Sciences › Mathematics › Numerical Analysis › Mathematical Approximation and Integration
- Keywords
- sensitivity index, variance decomposition, Sobol indices, global sensitivity analysis
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