TY - GEN
T1 - Diet Modelling: Combining Mathematical Programming Models with Data-Driven Methods
AU - Ivancic, Ante
AU - Kanellopoulos, Argyris
AU - Geleijnse, Johanna M.
PY - 2020/1/29
Y1 - 2020/1/29
N2 - 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.
AB - 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.
KW - Consumer preferences
KW - Diet modelling
KW - Machine learning
U2 - 10.1007/978-3-030-39815-6_7
DO - 10.1007/978-3-030-39815-6_7
M3 - Conference paper
SN - 9783030398149
T3 - IFIP Advances in Information and Communication Technology
SP - 72
EP - 80
BT - International Symposium on Environmental Software Systems (ISESS 2020)
PB - Springer
CY - Wageningen
ER -
