RT Journal Article
T1 Approaches to learning strictly-stable weights for data with missing values
A1 Beliakov, G.
A1 Gómez González, Daniel
A1 Jameson, Simon S.
A1 Montero De Juan, Francisco Javier
A1 Rodríguez González, Juan Tinguaro
AB The problem of missing data is common in real-world applications of supervised machine learning such as classification and regression. Such data often gives rise to the need for functions defined for varying dimension. Here we propose optimization methods for learning the weights of quasi-arithmetic means in the context of data with missing values. We investigate some alternative approaches depending on the number of variables that have missing values and show results for several numerical experiments.
PB Elsevier Science Bv
SN 0165-0114
YR 2017
FD 2017
LK https://hdl.handle.net/20.500.14352/18125
UL https://hdl.handle.net/20.500.14352/18125
LA eng
NO Beliakov, G., Gómez, D., James, S., Montero, J., Rodríguez, J.T.: Approaches to learning strictly-stable weights for data with missing values. Fuzzy Sets and Systems. 325, 97-113 (2017). https://doi.org/10.1016/j.fss.2017.02.003
DS Docta Complutense
RD 15 dic 2025