- Tytuł:
- An outlier-robust neuro-fuzzy system for classification and regression
- Autorzy:
- Siminski, Krzysztof
- Powiązania:
- https://bibliotekanauki.pl/articles/1838201.pdf
- Data publikacji:
- 2021
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
outliers
neuro-fuzzy system
clustering algorithm
regression
wyjątki
system neurorozmyty
algorytm grupowania - Opis:
- Real life data often suffer from non-informative objects—outliers. These are objects that are not typical in a dataset and can significantly decline the efficacy of fuzzy models. In the paper we analyse neuro-fuzzy systems robust to outliers in classification and regression tasks. We use the fuzzy c-ordered means (FCOM) clustering algorithm for scatter domain partition to identify premises of fuzzy rules. The clustering algorithm elaborates typicality of each object. Data items with low typicalities are removed from further analysis. The paper is accompanied by experiments that show the efficacy of our modified neuro-fuzzy system to identify fuzzy models robust to high ratios of outliers.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2021, 31, 2; 303-319
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł