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Fuzzy integrals for edge detection
(Springer, 2023)
Contribución a congreso / Biltzarrerako ekarpena,
In this work, we compare different families of fuzzy integrals
in the context of feature aggregation for edge detection. We analyze the
behaviour of the Sugeno and Choquet integral and some of its generalizations.
In ...
General grouping functions
(Springer, 2020)
info:eu-repo/semantics/conferenceObject,
Some aggregation functions that are not necessarily associative, namely overlap and grouping functions, have called the attention of many researchers in the recent past. This is probably due to the fact that they are a ...
dCF-integrals: generalizing CF-integrals by means of restricted dissimilarity functions
(IEEE, 2022)
Artículo / Artikulua,
The Choquet integral (CI) is an averaging aggregation function that has been used, e.g., in the fuzzy reasoning method (FRM) of fuzzy rule-based classification systems (FRBCSs) and in multicriteria decision making in order ...
Enhancing the efficiency of the interval-valued fuzzy rule-based classifier with tuning and rule selection
(Springer, 2020)
info:eu-repo/semantics/conferenceObject,
Interval-Valued fuzzy rule-based classifier with TUning and Rule Selection, IVTURS, is a state-of-the-art fuzzy classifier. One of the key point of this method is the usage of interval-valued restricted equivalence functions ...
Constructing interval-valued fuzzy material implication functions derived from general interval-valued grouping functions
(IEEE, 2022)
Contribución a congreso / Biltzarrerako ekarpena,
Grouping functions and their dual counterpart,
overlap functions, have drawn the attention of many authors,
mainly because they constitute a richer class of operators compared to other types of aggregation functions. ...
On the normalization of interval data
(MDPI, 2020)
info:eu-repo/semantics/article,
The impreciseness of numeric input data can be expressed by intervals. On the other hand, the normalization of numeric data is a usual process in many applications. How do we match the normalization with impreciseness on ...
Generalizing max pooling via (a, b)-grouping functions for convolutional neural networks
(Elsevier, 2023)
Artículo / Artikulua,
Due to their high adaptability to varied settings and effective optimization algorithm, Convolutional Neural
Networks (CNNs) have set the state-of-the-art on image processing jobs for the previous decade. CNNs work in
a ...
A generalization of the Sugeno integral to aggregate interval-valued data: an application to brain computer interface and social network analysis
(Elsevier, 2022)
Artículo / Artikulua,
Intervals are a popular way to represent the uncertainty related to data, in which we express the vagueness of each observation as the width of the interval. However, when using intervals for this purpose, we need to use ...
N-dimensional admissibly ordered interval-valued overlap functions and its influence in interval-valued fuzzy rule-based classification systems
(IEEE, 2021)
info:eu-repo/semantics/article,
Overlap functions are a type of aggregation functions that are not required to be associative, generally used to indicate the overlapping degree between two values. They have been successfully used as a conjunction operator ...
General admissibly ordered interval-valued overlap functions
(CEUR Workshop Proceedings (CEUR-WS.org), 2021)
info:eu-repo/semantics/conferenceObject,
Overlap functions are a class of aggregation functions that measure the verlapping degree between two values. They have been successfully applied in several problems in which associativity is not required, such as ...