- Tytuł:
- Comparison of incomplete data handling techniques for neuro-fuzzy systems
- Autorzy:
-
Sikora, M.
Simiński, K. - Powiązania:
- https://bibliotekanauki.pl/articles/305722.pdf
- Data publikacji:
- 2014
- Wydawca:
- Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
- Tematy:
-
incomplete data
marginalization
imputation
neuro-fuzzy system
ANNBFIS
PDS
IFCM
OCS
NPS - Opis:
- Real-life data sets sometimes miss some values. The incomplete data needs specialized algorithms or preprocessing that allows the use of the algorithms for complete data. The paper presents a comparison of various techniques for handling incomplete data in the neuro-fuzzy system ANNBFIS. The crucial procedure in the creation of a fuzzy model for the neuro-fuzzy system is the partition of the input domain. The most popular approach (also used in the ANNBFIS) is clustering. The analyzed approaches for clustering incomplete data are: preprocessing (marginalization and imputation) and specialized clustering algorithms (PDS, IFCM, OCS, NPS). The objective of our research is the comparison of the preprocessing techniques and specialized clustering algorithms to find the the most-advantageous technique for handling incomplete data with a neuro-fuzzy system. This approach is also the indirect validation of clustering.
- Źródło:
-
Computer Science; 2014, 15 (4); 441-458
1508-2806
2300-7036 - Pojawia się w:
- Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł