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Fitting aggregation functions to data: Part II - idempotization
conference contribution
posted on 2016-06-11, 00:00 authored by M Bartoszuk, Gleb BeliakovGleb Beliakov, Marek Gagolewski, Simon JamesSimon JamesThe 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.
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Source
Information processing and management of uncertainty in knowledge-based systemsVolume
611Series
Communications in computer and information sciencePagination
780 - 789Publisher
SpringerLocation
Eindhoven, The NetherlandsPlace of publication
Berlin, GermanyPublisher DOI
Start date
2016-06-20End date
2016-06-24ISSN
1865-0929ISBN-13
9783319405803Language
engNotes
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 publicationCopyright notice
2016, SpringerExtent
67Editor/Contributor(s)
J Carvalho, M Lesot, U Kaymak, S Vieira, B Bouchon-Meunier, R YagerTitle of proceedings
IPMU 2016 : Proceedings of the 16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based SystemsUsage metrics
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