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

Research output: Chapter in Book/Report/Conference proceedingConference paper

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.
Original languageEnglish
Title of host publicationInternational Symposium on Environmental Software Systems (ISESS 2020)
Subtitle of host publicationEnvironmental Software Systems. Data Science in Action
Place of PublicationWageningen
PublisherSpringer
Chapter7
Pages72-80
ISBN (Electronic)9783030398156
ISBN (Print)9783030398149
DOIs
Publication statusPublished - 29 Jan 2020

Publication series

NameIFIP Advances in Information and Communication Technology
Volume554
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

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Ivancic, A., Kanellopoulos, A., & Geleijnse, J. M. (2020). Diet Modelling: Combining Mathematical Programming Models with Data-Driven Methods. In International Symposium on Environmental Software Systems (ISESS 2020): Environmental Software Systems. Data Science in Action (pp. 72-80). (IFIP Advances in Information and Communication Technology ; Vol. 554). Wageningen: Springer. https://doi.org/10.1007/978-3-030-39815-6_7