The use of supervised learning techniques for fitting weights and/or generator functions of weighted quasi-arithmetic means – a special class of idempotent and nondecreasing aggregation functions – to empirical data has already been considered in a number of papers. Nevertheless, there are still some important issues that have not been discussed in the literature yet. In the first part of this two-part contribution we deal with the concept of regularization, a quite standard technique from machine learning applied so as to increase the fit quality on test and validation data samples. Due to the constraints on the weighting vector, it turns out that quite different methods can be used in the current framework, as compared to regression models. Moreover, it is worth noting that so far fitting weighted quasi-arithmetic means to empirical data has only been performed approximately, via the so-called linearization technique. In this paper we consider exact solutions to such special optimization tasks and indicate cases where linearization leads to much worse solutions.
History
Volume
611
Pagination
767-779
Location
Eindhoven, The Netherlands
Start date
2016-06-20
End date
2016-06-24
ISSN
1865-0929
ISBN-13
9783319405803
Language
eng
Notes
This publication is part II of the 16th IPMU International Conference held on 20-24 June 2016, Eindhoven, The Netherlands.
Publication classification
E Conference publication, E1 Full written paper - refereed
Copyright notice
2016, Springer
Extent
67
Editor/Contributor(s)
Carvalho J, Lesot M, Kaymak U, Vieira S, Bouchon-Meunier B, Yager R
Title of proceedings
IPMU 2016 : Proceedings of the 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems
Publisher
Springer
Place of publication
Berlin, Germany
Series
Communications in computer and information science