@article{ref1,
title="ProMetaUS: a proactive meta-learning uncertainty-based framework to select models for Dynamic Risk Management",
journal="Safety science",
year="2021",
author="Stefana, Elena and Paltrinieri, Nicola",
volume="138",
number="",
pages="e105238-e105238",
abstract="Safety managers, practitioners, and researchers can employ different models for estimating and assessing hazards, consequences, likelihoods, risks, and/or mitigation measures in the safety field. The selection of a specific model may depend on the uncertainty associated with its estimation and its impact on the safety-related decision-making process. The recognition of this issue as an example of Algorithm Selection Problem (ASP) allows investigating the applicability of meta-learning principles that are scarcely adopted in the risk and safety literature. Consequently, we propose a novel meta-learning inspired framework to proactively rank a set of candidate models for Dynamic Risk Management (DRM) based on desired uncertainty conditions. We denominate this framework ProMetaUS (Proactive Meta-learning and Uncertainty-based Selection for dynamic risk management). To achieve this purpose, our meta-learning system acquires knowledge that relates the characteristics extracted both directly and indirectly from datasets (e.g. data-based, domain-based, simple and fast uncertainty-based, simple and fast sensitivity-based meta-features) to some performance measures of the models. Performance measures include confidence information, shape measurable quantities, safety decision criteria and threshold limits, and sensitivity analysis outputs. We tested the proposed framework in a case study about Oxygen Deficiency Hazard (ODH) assessment by means of @RISK. For each of the five datasets, single-performance measure rankings and a final ranking of the three models are generated. Such rankings are aggregated to obtain the global recommended ranking.<p /> <p>Language: en</p>",
language="en",
issn="0925-7535",
doi="10.1016/j.ssci.2021.105238",
url="http://dx.doi.org/10.1016/j.ssci.2021.105238"
}