0640703 20260224163742.120251103235959.9 105010739860 001528199400006 10.22111/ijfs.2025.51019.9017 11 s. P cav_un_epca*06231551735-0654223 (2025)139-149 Optimization Concordance measure Copula Classification Correlation Association measure cav_un_auth*0436913 Sheikhi A. IR K cav_un_auth*0101163 Mesiar Radko UTIA-B Ekonometrie Department of Econometrics E E Department of Econometrics Ústav teorie informace a automatizace AV ČR, v. v. i. https://library.utia.cas.cz/separaty/2025/E/mesiar-0640703.pdf https://ijfs.usb.ac.ir/article_9284.html In the realm of classification studies, existing literature indicates that, when the relationships among exploratory variables extend beyond linear functions, nonlinear classifiers tend to outperform their linear counterparts. This study employs concordance measures to attain optimal outcomes in a classification task. In this regard, we examine the connection copula among the exploratory variables, as well as the copula linking the exploratory attributes to the target attribute are taken into consideration. As a major novelty, our classification approach utilizes a convex combination of the pairwise Spearman's rank correlation coefficient rho and the pairwise Kendall's association tau. Through a simulation analysis, we assess the performance of our algorithm, which demonstrates its superiority over alternatives, including copula-based classification methods as well as machine learning classification models. We also, provide an application of our method to the classification of COVID-19 dataset for more illustration. WOS BB 10000 10100 10103 2026 RVO:67985556 https://hdl.handle.net/11104/0371064 S A MATHEMATICS.APPLIED|MATHEMATICS 1.1 0.4 5.3 0.00071 0.225 821 39 0.34 38.05 1.55 351 Q2 1.000 66 Q1 71.1 1.11 Q1 83 2025 105010739860 SCOPUS PUBMED 001528199400006 WOS cav_un_epca*0623155 Iranian Journal of Fuzzy Systems 22 3 2025 139 149 1735-0654 2676-4334