ICrAData - Software for InterCriteria Analysis

Abstract

In this paper, we consider the InterCriteria Analysis (ICrA), which is based on the index matrices and intuitionistic fuzzy sets. We demonstrate the application of ICrA using the software ICrAData. ICrAData implements five different algorithms for InterCriteria relations calculation, namely: µ-biased, Unbiased, ν-biased, Balanced and Weighted. The software ICrAData displays results in two panels-matrix and graphical view, and the results can also be exported in various formats: matrices, vectors, and graphics. In the matrix view, the column data can be sorted in ascending or descending order. The graphic view has options for resizing the intuitionistic fuzzy triangle, showing a grid and assigning different colours to the points. Moreover, a selected point in the graphic is outlined in the matrix view, and vice-versa. In the present paper some of the ICrAData software functionalities are illustrated by an example.

Computational complexity and influence of numerical precision on the results of intercriteria analysis in the decision making process

“…The results of the ICrA method applied on these input data are as given in Table 3 (for the input data with precision of four decimal digits, which is the maximally available), Table 4 (for the input data with precision of three decimal digits), Table 5 (for the input data with precision of two decimal digits), and Table 6 (for the input data with precision of one decimal digit). The computations in all the four cases were made with both developed software packages [12,14], and yielded identical results per case. In each of the Tables 3-6, (a) stays for the membership parts of the intercriteria dependences, (b) stays for the table of the respective non-membership parts, and (c) goes for the calculated distances of the intercriteria points to the point (1, 0), i.e., the complete Truth, sorted in ascending way.…”

“…We will further note that two different software applications have been developed for the ICrA method, by Mavrov [14,15] and by Ikonomov [12], both being freely available from [13].…”

Inter-Criteria Analysis Based on Belief Functions for GPS Surveying Problems

“…Atanassov provides explicit formulas in [14] for K µ jj ′ and K ν jj ′ which depend on a particular choice of the signum function. Because of this the results of K µ jj ′ and K ν jj ′ are disputable and that is why some authors [22], [28] propose other methods to calculate K µ jj ′ and K ν jj ′ values for making the Inter-Criteria Analysis.…”

“…Remark 1: The construction of the Inter-Criteria Matrix K is not unique and depends on the choice of algorithm of construction of µ jj ′ and ν jj ′ (and the choice of the signum function) as reported in [28]. This can yield different ICrA results in general.…”

InterCriteria Analysis applied to data from Euro Health Consumer Index for comparing the healthcare systems’ performance in time

“…The result includes aggregated matrix with degrees of membership and aggregated matrix with degrees of non-memberships. The application of ICA over Euro health consumer index is processed using ICrAData software [17]. The results from the Aggregated InterCriteria analysis appled to the three-dimensional dataset for healthcare systems have the following form (Table 1) pairs of countries in dissonance.…”