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

Version 2 2024-06-04, 03:29
Version 1 2017-04-03, 13:55
journal contribution
posted on 2024-06-04, 03:29 authored by Gleb BeliakovGleb Beliakov, D Gómez, Simon JamesSimon James, J Montero, JT Rodríguez
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.

History

Journal

Fuzzy Sets and Systems

Volume

325

Pagination

97-113

Location

Amsterdam, The Netherlands

ISSN

0165-0114

eISSN

1872-6801

Language

English

Publication classification

C Journal article, C1.1 Refereed article in a scholarly journal

Copyright notice

2017, Elsevier B.V.

Publisher

ELSEVIER SCIENCE BV