A constructive framework to define fusion functions with floating domains in arbitrary closed real intervals
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F22%3A00564676" target="_blank" >RIV/67985556:_____/22:00564676 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0020025522008878?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0020025522008878?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ins.2022.08.007" target="_blank" >10.1016/j.ins.2022.08.007</a>
Alternative languages
Result language
angličtina
Original language name
A constructive framework to define fusion functions with floating domains in arbitrary closed real intervals
Original language description
Fusion functions and their most important subclass, aggregation functions, have been successfully applied in fuzzy modeling. However, there are practical problems, such as classification via Convolutional Neural Networks (CNNs), where the data to be aggregated are not modeling membership degrees in the unit interval. In this scenario, systems could benefit from the application of operators defined in domains different from [0,1], although, presenting similar behavior of some aggregation functions whose subclasses are currently defined only in the fuzzy context (e.g., overlap functions and t-norms). So, the main objective of this paper is to present a general framework to characterize classes of fusion functions with floating domains, called (a,b)-fusion functions, defined on any closed real interval [a,b], based on classes of core fusion functions defined on [0,1]. The fundamental aspect of this framework is that the properties of a core fusion function are preserved in the context of the analogous (a,b)-fusion function. Construction methods are presented, and some properties are studied. We also introduce a framework to define fusion functions in which the inputs come from an interval [a,b] but the output is mapped on a possibly different interval [c,d]. Finally, we present an illustrative example in image classification via CNNs.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Information Sciences
ISSN
0020-0255
e-ISSN
1872-6291
Volume of the periodical
610
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
30
Pages from-to
800-829
UT code for WoS article
000860782400010
EID of the result in the Scopus database
2-s2.0-85135958796