68,450 research outputs found
NEW APPROACH TO INFORMATION AGGREGATION
In this paper new types of aggregation operators, namely absorbing-norms and parametric type of operator families called distance-based or evolutionary operators are introduced. Absorbing- norms are commutative, associative binary operators having an absorbing element from the uni! interval. A detailed discussion of properties and structure of these operators is given in the paper. Two types of distance-based operators are defined. The maximum and minimum distance operators with respect to e have the value of the element, which is farther, or nearer to e, respectively, where e is an arbitrary element of the unit interval [0,1]. The special cases e = O and e = 1lead to the max and min operators. The new operators are evolutionary types in the sense that if e is increasing starting from zero till e = 1 the min operator is developing into the max operator, while on the other side the max is transformed into the min operator. It is shown that the evolutionary operators can be constructed by means of min and max operators, which are also special cases of the operators. The maximum distance operators are special operators called uninorms and the minimum distance ones are absorbing-norms
Pareto Optimality and Strategy Proofness in Group Argument Evaluation (Extended Version)
An inconsistent knowledge base can be abstracted as a set of arguments and a
defeat relation among them. There can be more than one consistent way to
evaluate such an argumentation graph. Collective argument evaluation is the
problem of aggregating the opinions of multiple agents on how a given set of
arguments should be evaluated. It is crucial not only to ensure that the
outcome is logically consistent, but also satisfies measures of social
optimality and immunity to strategic manipulation. This is because agents have
their individual preferences about what the outcome ought to be. In the current
paper, we analyze three previously introduced argument-based aggregation
operators with respect to Pareto optimality and strategy proofness under
different general classes of agent preferences. We highlight fundamental
trade-offs between strategic manipulability and social optimality on one hand,
and classical logical criteria on the other. Our results motivate further
investigation into the relationship between social choice and argumentation
theory. The results are also relevant for choosing an appropriate aggregation
operator given the criteria that are considered more important, as well as the
nature of agents' preferences
Construction of aggregation operators with noble reinforcement
This paper examines disjunctive aggregation operators used in various recommender systems. A specific requirement in these systems is the property of noble reinforcement: allowing a collection of high-valued arguments to reinforce each other while avoiding reinforcement of low-valued arguments. We present a new construction of Lipschitz-continuous aggregation operators with noble reinforcement property and its refinements. <br /
On aggregation operators of transitive similarity and dissimilarity relations
Similarity and dissimilarity are widely used concepts. One of the most studied matters is their combination or aggregation. However, transitivity property is often ignored when aggregating despite being a highly important property, studied by many authors but from different points of view. We collect here some results in preserving transitivity when aggregating, intending to clarify the relationship between aggregation and transitivity and making it useful to design aggregation operators that keep transitivity property. Some examples of the utility of the results are also shown.Peer ReviewedPostprint (published version
Constraint-wish and satisfied-dissatisfied: an overview of two approaches for dealing with bipolar querying
In recent years, there has been an increasing interest in dealing with user preferences in flexible database querying, expressing both positive and negative information in a heterogeneous way. This is what is usually referred to as bipolar database querying. Different frameworks have been introduced to deal with such bipolarity. In this chapter, an overview of two approaches is given. The first approach is based on mandatory and desired requirements. Hereby the complement of a mandatory requirement can be considered as a specification of what is not desired at all. So, mandatory requirements indirectly contribute to negative information (expressing what the user does not want to retrieve), whereas desired requirements can be seen as positive information (expressing what the user prefers to retrieve). The second approach is directly based on positive requirements (expressing what the user wants to retrieve), and negative requirements (expressing what the user does not want to retrieve). Both approaches use pairs of satisfaction degrees as the underlying framework but have different semantics, and thus also different operators for criteria evaluation, ranking, aggregation, etc
Systematic approach to nonlinear filtering associated with aggregation operators. Part 1. SISO-filters
There are various methods to help restore an image from noisy distortions. Each technique has its advantages and disadvantages. Selecting the appropriate method plays a major role in getting the desired image. Noise removal or noise reduction can be done on an image by linear or nonlinear filtering. The more popular linear technique is based on average (on mean) linear operators. Denoising via linear filters normally does not perform satisfactorily since both noise and edges contain high frequencies. Therefore, any practical denoising model has to be nonlinear. In this work, we introduce and analyze a new class of nonlinear SISO-filters that have their roots in aggregation operator theory. We show that a large body of non-linear filters proposed to date constitute a proper subset of aggregation filters. (C) 2017 The Authors. Published by Elsevier Ltd.This work was supported by grants the RFBR No. 17-07-00886 and by Ural State Forest Engineering's Center of Excellence in "Quantum and Classical Information Technologies for Remote Sensing Systems"
Systematic approach to nonlinear filtering associated with aggregation operators. Part 2. Frechet MIMO-filters
Median filtering has been widely used in scalar-valued image processing as an edge preserving operation. The basic idea is that the pixel value is replaced by the median of the pixels contained in a window around it. In this work, this idea is extended onto vector-valued images. It is based on the fact that the median is also the value that minimizes the sum of distances between all grey-level pixels in the window. The Frechet median of a discrete set of vector-valued pixels in a metric space with a metric is the point minimizing the sum of metric distances to the all sample pixels. In this paper, we extend the notion of the Frechet median to the general Frechet median, which minimizes the Frechet cost function (FCF) in the form of aggregation function of metric distances, instead of the ordinary sum. Moreover, we propose use an aggregation distance instead of classical metric distance. We use generalized Frechet median for constructing new nonlinear Frechet MIMO-filters for multispectral image processing. (C) 2017 The Authors. Published by Elsevier Ltd.This work was supported by grants the RFBR No 17-07-00886, No 17-29-03369 and by Ural State Forest University Engineering's Center of Excellence in "Quantum and Classical Information Technologies for Remote Sensing Systems"
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