A classification using mixture of concordance measures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00640703" target="_blank" >RIV/67985556:_____/25:00640703 - isvavai.cz</a>

Výsledek na webu

<a href="https://ijfs.usb.ac.ir/article_9284.html" target="_blank" >https://ijfs.usb.ac.ir/article_9284.html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22111/ijfs.2025.51019.9017" target="_blank" >10.22111/ijfs.2025.51019.9017</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A classification using mixture of concordance measures

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A classification using mixture of concordance measures

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Iranian Journal of Fuzzy Systems

ISSN

1735-0654

e-ISSN

2676-4334

Svazek periodika

22

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

IR - Íránská islámská republika

Počet stran výsledku

11

Strana od-do

139-149

Kód UT WoS článku

001528199400006

EID výsledku v databázi Scopus

2-s2.0-105010739860