A New Partial Correlation Coefficient Technique Based on Intuitionistic Fuzzy Information and Its Pattern Recognition Application

Abstract

Computation of correlation coefficient among attributes of ordinary database is important especially in the classification and analysis of data. Due to the hesitations in the process of data classification, the idea of intuitionistic fuzzy data (IFD) is appropriate for a reliable classification. To achieve a dependable correlation, the construct of partial correlation coefficient based on IFD has been considered. The construct of partial correlation coefficient of intuitionistic fuzzy sets (PCCIFSs) is reasonable since correlation coefficients of intuitionistic fuzzy sets (CCIFSs) are limited in the sense that it only expressed linear association and direction of such relation between IFD without minding the effect of other IFD. On the contrary, partial correlation coefficient finds the exact association between any two IFD by muting the effect of other IFD which could sway the result of the correlation coefficient. In previous works, the idea of PCCIFSs was introduced based on the multivariate correlation model using empirical logit transform. Besides the fact that the outputs of multivariate correlation model are not always easy to interpret, the approach also never considered the three parameters of IFSs and does not use the values of CCIFSs for the computational process. With these setbacks, we are motivated to propose a novel approach of finding PCCIFSs by incorporating the three parameters of IFD based on a modified CCIFSs approach. A comparative analysis of the robust PCCIFSs approach and the existing approach is considered to justify the novel approach. An application of the new approach of PCCIFSs is considered in the case of pattern recognition where the patterns are represented as intuitionistic fuzzy data.

Decision Support System for Prioritization of Offshore Wind Farm Site by Utilizing Picture Fuzzy Combined Compromise Solution Group Decision Method

“…Afterward, the similarity-based approach is adopted to derive the importance of the experts. By the aid of the PF expert assessment matrices, the averaging assessment matrix is obtained by Equation (24), and then the similarity grade between the averaging assessment matrix and expert matrices are computed by Equation (25). Further, the weight of experts are figured by Equation ( 26), the results are shown as ν 1 = 0.2518, ν 2 = 0.2480, ν 3 = 0.2484, and ν 4 = 0.2519.…”

“…Afterwards, in order to more effectively characterize the uncertain preferences of experts, FS theory was validly extended and obtained uncertain information representation models that can deal with different practical situations, such as intuitionistic FS [13], interval valued intuitionistic FS [14], Pythagorean FS [15], q-rung orthopair FS [16], and spherical FS [17]. The above extensions have been successfully employed in various fields, such as uncertain decision analysis, practical application problem modeling, and so forth [18][19][20][21][22][23][24][25][26]. However, the above extended models based on FS theory only depict the uncertain preferences of experts from the perspectives of membership and nonmembership and cannot effectively depict inconsistent and incomplete information generated in practical problems.…”

Modern Approach in Pattern Recognition Using Circular Fermatean Fuzzy Similarity Measure for Decision Making with Practical Applications

“…Ye [16][17][18] introduced IF and interval-valued IF cosine SM and the Dice SM based on the deducted IF sets which help in the feld of pattern recognition. Nwokoro [19] developed the IF approach for predicting maternal outcomes, Zhou et al [20] applied the generalized IF similarity operator recognition principle, and Ejegwa [21] explored the IF correlation coefcient for classifcation process. Zhang [22] considered the IF score function for pattern recognition and medical diagnosis.…”