| bibtype |
J -
Journal Article
|
| ARLID |
0640703 |
| utime |
20251103075439.0 |
| mtime |
20251103235959.9 |
| SCOPUS |
105010739860 |
| WOS |
001528199400006 |
| DOI |
10.22111/ijfs.2025.51019.9017 |
| title
(primary) (eng) |
A classification using mixture of concordance measures |
| specification |
| page_count |
11 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0623155 |
| ISSN |
1735-0654 |
| title
|
Iranian Journal of Fuzzy Systems |
| volume_id |
22 |
| volume |
3 (2025) |
| page_num |
139-149 |
|
| keyword |
Optimization |
| keyword |
Concordance measure |
| keyword |
Copula |
| keyword |
Classification |
| keyword |
Correlation |
| keyword |
Association measure |
| author
(primary) |
| ARLID |
cav_un_auth*0436913 |
| name1 |
Sheikhi |
| name2 |
A. |
| country |
IR |
| garant |
K |
|
| author
|
| ARLID |
cav_un_auth*0101163 |
| name1 |
Mesiar |
| name2 |
Radko |
| institution |
UTIA-B |
| full_dept (cz) |
Ekonometrie |
| full_dept |
Department of Econometrics |
| department (cz) |
E |
| department |
E |
| full_dept |
Department of Econometrics |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| abstract
(eng) |
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. |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2026 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0371064 |
| confidential |
S |
| mrcbC91 |
A |
| mrcbT16-e |
MATHEMATICS.APPLIED|MATHEMATICS |
| mrcbT16-f |
1.1 |
| mrcbT16-g |
0.4 |
| mrcbT16-h |
5.3 |
| mrcbT16-i |
0.00071 |
| mrcbT16-j |
0.224 |
| mrcbT16-k |
821 |
| mrcbT16-q |
39 |
| mrcbT16-s |
0.34 |
| mrcbT16-y |
38.05 |
| mrcbT16-x |
1.55 |
| mrcbT16-3 |
351 |
| mrcbT16-4 |
Q2 |
| mrcbT16-5 |
1.000 |
| mrcbT16-6 |
66 |
| mrcbT16-7 |
Q1 |
| mrcbT16-C |
70.9 |
| mrcbT16-M |
1.12 |
| mrcbT16-N |
Q1 |
| mrcbT16-P |
82.7 |
| arlyear |
2025 |
| mrcbU14 |
105010739860 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001528199400006 WOS |
| mrcbU63 |
cav_un_epca*0623155 Iranian Journal of Fuzzy Systems 22 3 2025 139 149 1735-0654 2676-4334 |
|