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Diet Modelling: Combining Mathematical Programming Models with Data-Driven Methods

Abstract : Mathematical programming has been the principal workhorse behind most diet models since the 1940s. As a predominantly hypothesis-driven modelling paradigm, its structure is mostly defined by a priori information, i.e. expert knowledge. In this paper we consider two machine learning paradigms, and three instances thereof that could help leverage the readily available data and derive valuable insights for modelling healthier, and acceptable human diets.
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https://hal.inria.fr/hal-03361897
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Submitted on : Friday, October 1, 2021 - 3:41:18 PM
Last modification on : Wednesday, November 3, 2021 - 7:05:50 AM
Long-term archiving on: : Sunday, January 2, 2022 - 7:28:51 PM

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Ante Ivancic, Argyris Kanellopoulos, Johanna M. Geleijnse. Diet Modelling: Combining Mathematical Programming Models with Data-Driven Methods. 13th International Symposium on Environmental Software Systems (ISESS), Feb 2020, Wageningen, Netherlands. pp.72-80, ⟨10.1007/978-3-030-39815-6_7⟩. ⟨hal-03361897⟩

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