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oai:docta.ucm.es:20.500.14352/181252024-11-25T15:57:59Zcom_20.500.14352_14col_20.500.14352_15

Beliakov, G. Gómez González, Daniel Jameson, Simon S. Montero De Juan, Francisco Javier Rodríguez González, Juan Tinguaro 2023-06-17T22:08:35Z 2023-06-17T22:08:35Z 2017 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 0165-0114 10.1016/j.fss.2017.02.003 https://hdl.handle.net/20.500.14352/18125 https//doi.org/10.1016/j.fss.2017.02.003 http://www.sciencedirect.com/science/article/pii/S0165011417300635 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. eng restricted access Approaches to learning strictly-stable weights for data with missing values journal article