Handling augmented appraisal degrees from multiple perspectives with the universal operator X a,b,c,d,e,f

An augmented appraisal degree (AAD) is a generalization of a membership grade that indicates not only the level to which but also the reasons why a proposition is deemed to be true (or false). AADs as such can be used for the augmentation of the membership and non- membership components of each element of an intuitionistic fuzzy set (IFS). Such augmented IFSs have been proven to be useful for handling experience-based evaluations (XBEs) given by a heterogeneous group of people. In this paper, a semantic interpretation of the universal operator Xa,b,c,d,e,f , which is part of the IFS framework, is proposed as a novel option to obtain an approximation of how an AAD characterizing an XBE given by someone is perceived from the perspective of someone else. We illustrate how this interpretation can be applied to handle AADs – and thus XBEs – from multiple perspectives

Towards better concordance among contextualized evaluations in FAST-GDM problems

A flexible attribute-set group decision-making (FAST-GDM) problem consists in finding the most suitable option(s) out of the options under consideration, with a general agreement among a heterogeneous group of experts who can focus on different attributes to evaluate those options. An open challenge in FAST-GDM problems is to design consensus reaching processes (CRPs) by which the participants can perform evaluations with a high level of consensus. To address this challenge, a novel algorithm for reaching consensus is proposed in this paper. By means of the algorithm, called FAST-CR-XMIS, a participant can reconsider his/her evaluations after studying the most influential samples that have been shared by others through contextualized evaluations. Since exchanging those samples may make participants’ understandings more like each other, an increase of the level of consensus is expected. A simulation of a CRP where contextualized evaluations of newswire stories are characterized as augmented intuitionistic fuzzy sets (AIFS) shows how FAST-CR-XMIS can increase the level of consensus among the participants during the CRP

Contextualizing support vector machine predictions

Classification in artificial intelligence is usually understood as a process whereby several objects are evaluated to predict the class(es) those objects belong to. Aiming to improve the interpretability of predictions resulting from a support vector machine classification process, we explore the use of augmented appraisal degrees to put those predictions in context. A use case, in which the classes of handwritten digits are predicted, illustrates how the interpretability of such predictions is benefitted from their contextualization

On the need for augmented appraisal degrees to handle experience-based evaluations

Experience-based evaluations, i.e., evaluations resulting from what one has learned or understood about a particular topic by experience, are an important component in modern information management. This is especially the case when data from social media or crowdsourcing are involved. In this paper, techniques for handling and comparing experience-based (fuzzy) evaluations are proposed and studied. Since such evaluations could be fairly subjective, their comparison could be affected not only by the magnitude of each appraisal, but also by its context ---herein, by *‘context of an evaluation'* is meant the conditions that arise when the evaluation is carried out, which mainly depend on the experience of an evaluator about the topic under consideration. Therefore, to characterize in a better way the connotative meaning in each experience-based evaluation, an *augmented appraisal degree*, AAD for short, is proposed as a novel generalization of a membership (or non-membership) degree. Along with the definition of an AAD, an augmented framework is described. The augmented framework includes several concepts, operators and functions that support different methods of computation with (collections of) AADs. We pay special attention to the description, use, potential benefits and applications of this augmented framework