Fitting aggregation functions to data: Part II - idempotization

Bartoszuk, M, Beliakov, Gleb, Gagolewski, Marek and James, Simon 2016, Fitting aggregation functions to data: Part II - idempotization, in IPMU 2016 : Proceedings of the 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Springer, Berlin, Germany, pp. 780-789, doi: 10.1007/978-3-319-40581-0_63.

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Title Fitting aggregation functions to data: Part II - idempotization
Author(s) Bartoszuk, M
Beliakov, GlebORCID iD for Beliakov, Gleb
Gagolewski, MarekORCID iD for Gagolewski, Marek
James, SimonORCID iD for James, Simon
Conference location Eindhoven, The Netherlands
Conference dates 2016/06/20 - 2016/06/24
Title of proceedings IPMU 2016 : Proceedings of the 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems
Editor(s) Carvalho, J
Lesot, M
Kaymak, U
Vieira, S
Bouchon-Meunier, B
Yager, R
Publication date 2016
Series Communications in computer and information science
Start page 780
End page 789
Total pages 9
Publisher Springer
Place of publication Berlin, Germany
Keyword(s) aggregation functions
weighted quasi-arithmetic means
least squares fitting
Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Computer Science
Summary 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.
Notes This publication is included in part II of the 16th IPMU International Conference held 20-24 Jun 2016 in Eindhoven, The Netherlands.
ISBN 9783319405803
ISSN 1865-0929
Language eng
DOI 10.1007/978-3-319-40581-0_63
Indigenous content off
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, Springer
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