Fitting aggregation functions to data: Part II - idempotization

Bartoszuk, M, Beliakov, Gleb, Gagolewski, M 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 orcid.org/0000-0002-9841-5292
Gagolewski, M
James, SimonORCID iD for James, Simon orcid.org/0000-0003-1150-0628
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 2016-06-11
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
idempotence
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Computer Science
OPERATORS
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
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
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085791

Document type: Conference Paper
Collection: School of Information Technology
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