期刊
PLOS ONE
卷 17, 期 2, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0263150
关键词
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资金
- Medical Research Council [MC_UU_00022/1]
- Chief Scientist Office [SPHSU16]
- UKRI EPSRC through a Turing Network Development Award
- UKRI Research England's THYME project
- Children's Liver Disease Foundation
- UK Prevention Research Partnership [MR/S037594/1]
- British Heart Foundation
- Cancer Research UK
- Chief Scientist Office of the Scottish Government Health and Social Care Directorates
- Engineering and Physical Sciences Research Council
- Economic and Social Research Council
- Health and Social Care Research and Development Division (Welsh Government)
- Medical Research Council
- National Institute for Health Research
- Natural Environment Research Council
- Public Health Agency (Northern Ireland)
- Health Foundation
- Wellcome
- Chief Scientist Office [SPHSU16] Funding Source: researchfish
- Medical Research Council [MC_UU_00022/1] Funding Source: researchfish
This study compared the performance of multiple machine-learning methods for surrogate modelling in agent-based models (ABMs). The results showed that artificial neural networks (ANNs) and gradient-boosted trees outperformed Gaussian process surrogates, the most commonly used method. Using machine-learning methods for surrogate modelling in ABMs can reduce CPU time consumption and facilitate more robust sensitivity analyses.
In this proof-of-concept work, we evaluate the performance of multiple machine-learning methods as surrogate models for use in the analysis of agent-based models (ABMs). Analysing agent-based modelling outputs can be challenging, as the relationships between input parameters can be non-linear or even chaotic even in relatively simple models, and each model run can require significant CPU time. Surrogate modelling, in which a statistical model of the ABM is constructed to facilitate detailed model analyses, has been proposed as an alternative to computationally costly Monte Carlo methods. Here we compare multiple machine-learning methods for ABM surrogate modelling in order to determine the approaches best suited as a surrogate for modelling the complex behaviour of ABMs. Our results suggest that, in most scenarios, artificial neural networks (ANNs) and gradient-boosted trees outperform Gaussian process surrogates, currently the most commonly used method for the surrogate modelling of complex computational models. ANNs produced the most accurate model replications in scenarios with high numbers of model runs, although training times were longer than the other methods. We propose that agent-based modelling would benefit from using machine-learning methods for surrogate modelling, as this can facilitate more robust sensitivity analyses for the models while also reducing CPU time consumption when calibrating and analysing the simulation.
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