Mixture Functions Based on Deviation and Dissimilarity Functions

Špirková, Jana; Kráľ, Pavol

doi:10.1007/978-3-030-19494-9_24

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…It is worth mentioning that via deviation-based approaches there is possible to catch several types of mixture operators and their generalizations (more in [17], [18], [19], [20], [21]).…”

Section: Preliminariesmentioning

confidence: 99%

A fusion method for multi-valued data

et al. 2021

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“…It is worth mentioning that via deviation-based approaches there is possible to catch several types of mixture operators and their generalizations (more in [17], [18], [19], [20], [21]).…”

Section: Preliminariesmentioning

confidence: 99%

A fusion method for multi-valued data

et al. 2021

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“…Another example is the transformations of aggregation functions such as flippings [14,24], polynomial transformations [3,6,10,33,40,42,41], compositions [18,31] and others [27,28,30]. Penalty-based constructions also gain interest in recent years [1,4,5,13,12,23,35,36,39,42,38].…”

Section: Introductionmentioning

confidence: 99%

Aggregation Function Constructed from Copula

BOONYASRI¹,

Tasena²

2022

CJM

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"In this work, we show that a slight change in the Sklar's formula tremendously affects the class of aggregation functions it represents. While the original formula can only be used to construct $d$-increasing aggregation functions, this new formula can be used to construct any continuous aggregation function excepted possibly those belong to the boundary of this set. In particular, all continuous aggregation functions can be approximated by aggregation functions in this form. This shows that it is sufficient to only consider aggregation functions in this form for most cases. Construction examples via this method are also given."

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“…The main goal of this aggregation is to obtain such an output value that characterizes the whole set of inputs as concisely as possible. However, mentioned Daróczy's means do not always maintain monotonicity, i.e., they are not, in general, aggregation functions, [5,6,10]. Therefore, the authors in [5,6] introduced a so-called moderate deviation function, which ensures that the aggregation functions based on the moderate deviation functions meet all the properties of aggregation functions.…”

Section: Introductionmentioning

confidence: 99%

New Classes of the Moderate Deviation Functions

Špirková

Bustince

Fernández³

et al. 2021

Atlantis Studies in Uncertainty Modelling

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At present, in the field of aggregation of various input values, attention is focused on the construction of aggregation functions using other functions that can affect the resulting aggregated value. This resulting value should characterize the properties of the individual input values as accurately as possible. Attention is also paid to aggregation using the so-called moderate deviation function. Using this function in aggregation ensures that all properties of aggregation functions are preserved. This work offers constructions of the moderate deviation functions using negations and automorphisms on the symmetric interval [−1, 1] and a general closed interval [a, b] ⊂ [−∞, ∞].

show abstract

Mixture Functions Based on Deviation and Dissimilarity Functions

Cited by 5 publications

References 12 publications

A fusion method for multi-valued data

A fusion method for multi-valued data

Aggregation Function Constructed from Copula

New Classes of the Moderate Deviation Functions

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