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Obiekt
Tytuł: Benchmarking imputation methods for fuzzy datasets
Contributor:
Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.
Group publication title:
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.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Resource Identifier:
DOI:
Pages:
Source:
AMCS, volume 36, number 2 (2026) ; kliknij tutaj, żeby przejść
Language:
License CC BY 4.0:
Rights:
Biblioteka Uniwersytetu Zielonogórskiego
Kolekcje, do których przypisany jest obiekt:
- 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)
Data ostatniej modyfikacji:
Jun 17, 2026
Data dodania obiektu:
Jun 17, 2026
Liczba wyświetleń treści obiektu:
21
Wszystkie dostępne wersje tego obiektu:
https://www.zbc.uz.zgora.pl/repozytorium/publication/108332
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| Nazwa wydania | Data |
|---|---|
| Benchmarking imputation methods for fuzzy datasets | Jun 17, 2026 |
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