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.
|Title of host publication||International Symposium on Environmental Software Systems (ISESS 2020)|
|Subtitle of host publication||Environmental Software Systems. Data Science in Action|
|Place of Publication||Wageningen|
|Publication status||Published - 29 Jan 2020|
|Name||IFIP Advances in Information and Communication Technology|
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