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

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Beliakov, G. and Gomez, D. and Jameson, Simon S and Montero, Javier and Rodríguez, Juan Tinguaro (2017) Approaches to learning strictly-stable weights for data with missing values. Fuzzy Sets and Systems, 325 . pp. 97-113. ISSN 0165-0114

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Official URL: http://www.sciencedirect.com/science/article/pii/S0165011417300635


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Abstract

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.


Item Type:Article
Uncontrolled Keywords:Aggregation functions; Strict stability; Missing data; Weight learning; Linear programming
Subjects:Sciences > Mathematics > Logic, Symbolic and mathematical
ID Code:44881
Deposited On:04 Oct 2017 15:06
Last Modified:04 Oct 2017 15:06

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