Approaches to learning strictly-stable weights for data with missing values

Beliakov, G.Gómez González, DanielJameson, Simon S.Montero De Juan, Francisco JavierRodríguez González, Juan Tinguaro2023-06-172023-06-172017Beliakov, 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.2017.02.0030165-011410.1016/j.fss.2017.02.003https://hdl.handle.net/20.500.14352/18125The 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.engApproaches to learning strictly-stable weights for data with missing valuesjournal articlehttps//doi.org/10.1016/j.fss.2017.02.003http://www.sciencedirect.com/science/article/pii/S0165011417300635restricted access510.6Aggregation functionsStrict stabilityMissing dataWeight learningLinear programmingLógica simbólica y matemática (Matemáticas)1102.14 Lógica Simbólica