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Fitting aggregation functions to data: Part II - idempotization

Bartoszuk, Maciej, Beliakov, Gleb, Gagolewski, Marek and James, Simon 2016, Fitting aggregation functions to data: Part II - idempotization. In Carvalho, Joao Paulo, Lesot, Marie-Jeanne, Kaymak, Uzay, Vieira, Susana, Bouchon-Meunier, Bernadette and Yager, Ronald R. (ed), 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, Maciej
Beliakov, GlebORCID iD for Beliakov, Gleb
Gagolewski, Marek
James, SimonORCID iD for James, Simon
Title of book Information processing and management of uncertainty in knowledge-based systems
Editor(s) Carvalho, Joao Paulo
Lesot, Marie-Jeanne
Kaymak, Uzay
Vieira, Susana
Bouchon-Meunier, Bernadette
Yager, Ronald R.
Publication date 2016
Series Communications in computer and information science
Chapter number 63
Total chapters 67
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
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 B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2016, Springer
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Document type: Book Chapter
Collection: School of Information Technology
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