Entropy techniques for robust management decision making in high-dimensional data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F24%3A00602743" target="_blank" >RIV/67985807:_____/24:00602743 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/24:10490141

Výsledek na webu

<a href="https://doi.org/10.5937/sjm19-48723" target="_blank" >https://doi.org/10.5937/sjm19-48723</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5937/sjm19-48723" target="_blank" >10.5937/sjm19-48723</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Entropy techniques for robust management decision making in high-dimensional data

Popis výsledku v původním jazyce

Entropy, a key measure of chaos or diversity, has recently found intriguing applications in the realm of management science. Traditional entropy-based approaches for data analysis, however, prove inadequate when dealing with high-dimensional datasets. In this paper, a novel uncertainty coefficient based on entropy is proposed for categorical data, together with a pattern discovery method suitable for management applications. Furthermore, we present a robust fractal-inspired technique for estimating covariance matrices in multivariate data. The efficacy of this method is thoroughly examined using three real datasets with economic relevance. The results demonstrate the superior performance of our approach, even in scenarios involving a limited number of variables. This suggests that managerial decision-making processes should reflect the inherent fractal structure present in the given multivariate data. The work emphasizes the importance of considering fractal characteristics in managerial decision-making, thereby advancing the applicability and effectiveness of entropy-based methods in management science.

Název v anglickém jazyce

Entropy techniques for robust management decision making in high-dimensional data

Popis výsledku anglicky

Entropy, a key measure of chaos or diversity, has recently found intriguing applications in the realm of management science. Traditional entropy-based approaches for data analysis, however, prove inadequate when dealing with high-dimensional datasets. In this paper, a novel uncertainty coefficient based on entropy is proposed for categorical data, together with a pattern discovery method suitable for management applications. Furthermore, we present a robust fractal-inspired technique for estimating covariance matrices in multivariate data. The efficacy of this method is thoroughly examined using three real datasets with economic relevance. The results demonstrate the superior performance of our approach, even in scenarios involving a limited number of variables. This suggests that managerial decision-making processes should reflect the inherent fractal structure present in the given multivariate data. The work emphasizes the importance of considering fractal characteristics in managerial decision-making, thereby advancing the applicability and effectiveness of entropy-based methods in management science.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Serbian Journal of Management

ISSN

1452-4864

e-ISSN

1452-4864

Svazek periodika

19

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

RS - Srbská republika

Počet stran výsledku

13

Strana od-do

471-483

Kód UT WoS článku

001376541500012

EID výsledku v databázi Scopus

2-s2.0-85211383010