Use of idempotent functions in the aggregation of different filters for noise removal

The majority of existing denoising algorithms obtain good results for a specific noise model, and when it is known previously. Nonetheless, there is a lack in denoising algorithms that can deal with any unknown noisy images. Therefore, in this paper, we study the use of aggregation functions for denoising purposes, where the noise model is not necessary known in advance; and how these functions affect the visual and quantitative results of the resultant images

Positional voting rules generated by aggregation functions and the role of duplication

Producción CientíficaIn this paper, we consider a typical voting situation where a group of agents show their preferences over a set of alternatives. Under our approach, such preferences are codied into individual positional values which can be aggregated in several ways through particular functions, yielding positional voting rules and providing a social result in each case. We show that scoring rules belong to such class of positional voting rules. But if we focus our interest on OWA operators as aggregation functions, other well-known voting systems naturally appear. In particular, we determine those ones verifying duplication (i.e., clone irrelevance) and present a proposal of an overall social result provided by them.Ministerio de Economía, Industria y Competitividad (ECO2012-32178)Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA066U13

Weighted‐selective aggregated majority‐OWA operator and its application in linguistic group decision making

This paper focuses on the aggregation operations in the group decision-making model based on the concept of majority opinion. The weighted-selective aggregated majority-OWA (WSAM-OWA) operator is proposed as an extension of the SAM-OWA operator, where the reliability of information sources is considered in the formulation. The WSAM-OWA operator is generalized to the quanti- fied WSAM-OWA operator by including the concept of linguistic quantifier, mainly for the group fusion strategy. The QWSAM-IOWA operator, with an ordering step, is introduced to the individual fusion strategy. The proposed aggregation operators are then implemented for the case of alternative scheme of heterogeneous group decision analysis. The heterogeneous group includes the consensus of experts with respect to each specific criterion. The exhaustive multicriteria group decision-making model under the linguistic domain, which consists of two-stage aggregation processes, is developed in order to fuse the experts' judgments and to aggregate the criteria. The model provides greater flexibility when analyzing the decision alternatives with a tolerance that considers the majority of experts and the attitudinal character of experts. A selection of investment problem is given to demonstrate the applicability of the developed model

A Consensus Approach to the Sentiment Analysis Problem Driven by Support-Based IOWA Majority

In group decision making, there are many situations where the opinion of the majority of participants is critical. The scenarios could be multiple, like a number of doctors finding commonality on the diagnose of an illness or parliament members looking for consensus on an specific law being passed. In this article, we present a method that utilizes induced ordered weighted averaging (IOWA) operators to aggregate a majority opinion from a number of sentiment analysis (SA) classification systems, where the latter occupy the role usually taken by human decision-makers as typically seen in group decision situations. In this case, the numerical outputs of different SA classification methods are used as input to a specific IOWA operator that is semantically close to the fuzzy linguistic quantifier ‘most of’. The object of the aggregation will be the intensity of the previously determined sentence polarity in such a way that the results represent what the majority think. During the experimental phase, the use of the IOWA operator coupled with the linguistic quantifier ‘most’ (math formula) proved to yield superior results compared to those achieved when utilizing other techniques commonly applied when some sort of averaging is needed, such as arithmetic mean or median techniques

OWA-based aggregation operations in multi-expert MCDM model

This paper presents an analysis of multi-expert multi-criteria decision making (ME-MCDM) model based on the ordered weighted averaging (OWA) operators. Two methods of modeling the majority opinion are studied as to aggregate the experts' judgments, in which based on the induced OWA operators. Then, an overview of OWA with the inclusion of different degrees of importance is provided for aggregating the criteria. An alternative OWA operator with a new weighting method is proposed which termed as alternative OWAWA (AOWAWA) operator. Some extensions of ME-MCDM model with respect to two-stage aggregation processes are developed based on the classical and alternative schemes. A comparison of results of different decision schemes then is conducted. Moreover, with respect to the alternative scheme, a further comparison is given for different techniques in integrating the degrees of importance. A numerical example in the selection of investment strategy is used as to exemplify the model and for the analysis purpose

GIS-Based Local Ordered Weighted Averaging: A Case Study in London, Ontario

GIS-based multicriteria analysis is a procedure for combining a set of criterion maps and associated criterion weights to obtain overall value for each spatial unit (location) in the study area. Ordered Weighted Averaging (OWA) is a generic algorithm of the multicriteria analysis. It has been integrated into GIS and applied for tackling a wide range of spatial problems. However, the conventional OWA method is based on an assumption of spatial homogeneity of its parameters. Therefore, it is referred to as a global model. This thesis proposes a local form OWA. The local model is based on the range sensitivity principle. A case study of examining spatial patterns of socioeconomic status in London, Ontario is presented. The results show that there are substantial differences between the spatial patterns generated by the global and local OWA methods