Entropy techniques for robust management decision making in high-dimensional data
| Autor: | Jan Kalina |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Serbian Journal of Management, Vol 19, Iss 2, Pp 471-483 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1452-4864 2217-7159 |
| DOI: | 10.5937/sjm19-4872 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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