Usability of Concordance Indices in FAST-GDM Problems

Towards Better Concordance among Contextualized Evaluations in FAST-GDM Problems

“…A way to compute a concordance index between the individual and collective evaluations is by means of a function S(⋅, ⋅) that computes the similarity between the AIFSs that represent those evaluations [28], i.e., the concordance index between @E j and can be computed through the expression cix( @E j ,Â) = S( @E j ,Â).…”

“…Functions that compute the similarity between two IFSs, say J and A, have been proposed in [28] to compute the concordance index between the individual and collective evaluations. Among those functions, one can find the following proposed in [33]:…”

“…It is worth mentioning that the flat operator, ⋅ ⊩, which turns an AIFS into an IFS by excluding the feature collections contained in each of its elements [28], can be used for converting @E j and into IFSs J and A respectively.…”

“…To quantify the level of consensus in a CRP, a moderator can be supported by indices, e.g., a concordance index between the evaluations given by the participants [27]. In the case of FAST-GDM, the usability of several theoretical concordance indices has been studied in [28].…”

Explaining Computer Predictions with Augmented Appraisal Degrees

“…For example, the evaluation of the proposition "the handwritten number depicted in Figure 1(a) is a '6' " can be denoted by x, 0.23, 0.37 . Regarding the comparison of two evaluations characterized by two IFS elements, say x, µ A (x), ν A (x) and x, µ B (x), ν B (x) , one can first compute the buoyancy of each IFS el- [13] and then compare the resulting values. Thus, e.g., ρ A (x) > ρ B (x) suggests that x belongs more to A than to B.…”