- Tytuł:
- Rule modeling of ADI cast iron structure for contradictory data
- Autorzy:
-
Soroczyński, Artur
Biernacki, Robert
Kochański, Andrzej - Powiązania:
- https://bibliotekanauki.pl/articles/29520059.pdf
- Data publikacji:
- 2022
- Wydawca:
- Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
- Tematy:
-
rule modeling
contradictory data set
uncertainty
data preparation
decision tree
rough set theory
niepewność
drzewo decyzyjne
teoria zbiorów przybliżonych - Opis:
- Ductile iron is a material that is very sensitive to the conditions of crystallization. Due to this fact, the data on the cast iron properties obtained in tests are significantly different and thus sets containing data from samples are contradictory, i.e. they contain inconsistent observations in which, for the same set of input data, the output values are significantly different. The aim of this work is to try to determine the possibility of building rule models in conditions of significant data uncertainty. The paper attempts to determine the impact of the presence of contradictory data in a data set on the results of process modeling with the use of rule-based methods. The study used the well-known dataset (Materials Algorithms Project Data Library, n.d.) pertaining to retained austenite volume fraction in austempered ductile cast iron. Two methods of rulebased modeling were used to model the volume of the retained austenite: the decision trees algorithm (DT) and the rough sets algorithm (RST). The paper demonstrates that the number of inconsistent observations depends on the adopted data discretization criteria. The influence of contradictory data on the generation of rules in both algorithms is considered, and the problems that can be generated by contradictory data used in rule modeling are indicated.
- Źródło:
-
Computer Methods in Materials Science; 2022, 22, 4; 211-228
2720-4081
2720-3948 - Pojawia się w:
- Computer Methods in Materials Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł