Multiple surrogates: how cross-validation errors can help us to obtain the best predictor

431 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? (2014, background): “Recent works such as [102] and [105] extend the idea to selection of models”
Surrogate-based methods for black-box optimization (2017, background): “This approach acts like an insurance policy against poorly ﬁtted models [56].”
Using Cross Validation to Design Conservative Surrogates (2010, methodology): “We could easily generate a large set of surrogates and select the most accurate based on cross validation [29].”
Crashworthiness optimization of foam-filled tapered thin-walled structure using multiple surrogate models (2013, methodology): “It must be pointed out that these different surrogate models often provide rather different modeling accuracy (Viana et al. 2009), gradient information and design outcomes (Wang et al.”
Predictive quantification of surrogate model fidelity based on modal variations with sample density (2015, methodology): “Viana et al. (2009) applied the RMSECV approach as a criterion in surrogate model selection and in constructing a weighted average surrogate.”