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Fitting aggregation functions to data: Part II - idempotization

Version 2 2024-06-04, 03:29
Version 1 2016-08-31, 12:09
conference contribution
posted on 2024-06-04, 03:29 authored by M Bartoszuk, Gleb BeliakovGleb Beliakov, M Gagolewski, Simon JamesSimon James
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 second part of this two-part contribution we deal with a quite common situation in which we have inputs coming from different sources, describing a similar phenomenon, but which have not been properly normalized. In such a case, idempotent and nondecreasing functions cannot be used to aggregate them unless proper preprocessing is performed. The proposed idempotization method, based on the notion of B-splines, allows for an automatic calibration of independent variables. The introduced technique is applied in an R source code plagiarism detection system.

History

Volume

611

Pagination

780-789

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 included in part II of the 16th IPMU International Conference held 20-24 Jun 2016 in Eindhoven, The Netherlands.

Publication classification

E1 Full written paper - refereed, E Conference publication

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