Digital Library of Zielona Góra contains 9 465 digital objects
Object
Title: Benchmarking imputation methods for fuzzy datasets
Współtwórca:
Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.
Tytuł publikacji grupowej:
Abstract:
Imputation methods are widely used to replace missing values in datasets, thereby improving the overall quality of samples and enabling further statistical procedures. Various measures and tools have been proposed to compare the effectiveness and results of imputation algorithms. This paper describes the extended benchmarking approach designed explicitly for imputing fuzzy datasets. It is intended as a unique combination of classical tools with new measures that address the special features of fuzzy sets. ; With the help of this benchmark, five imputation methods (the widely known missForest, miceRanger, kNN, and PMM algorithms, as well as the dimp method aimed specifically for fuzzy data) are numerically compared using various synthetic, real-life, single- and multivariate datasets. It is the first such comparison explicitly related to fuzzy data. The obtained conclusions shed new light on the existing, yet still overlooked, problem of imputing missing fuzzy data.
Wydawca:
Zielona Góra: Uniwersytet Zielonogórski
Identyfikator zasobu:
DOI:
Strony:
Źródło:
AMCS, volume 36, number 2 (2026) ; click here to follow the link
Jezyk:
Licencja CC BY 4.0:
Prawa do dysponowania publikacją:
Biblioteka Uniwersytetu Zielonogórskiego
Object collections:
- Repository > Faculties > Faculty of Computer, Electrical and Control Engineering
- Repository > Types of work > Articles
- Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (2026)
Last modified:
17 cze 2026
In our library since:
17 cze 2026
Number of object content hits:
5
All available object's versions:
https://www.zbc.uz.zgora.pl/repozytorium/publication/108332
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Show description in OAI-PMH format:
| Edition name | Date |
|---|---|
| Benchmarking imputation methods for fuzzy datasets | 17 cze 2026 |
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