RT journal article
T1 Decision Making in Fuzzy Rough Set Theory
A1 Chacón Gómez, Fernando
A1 Cornejo Piñero, María Eugenia
A1 Medina Moreno, Jesús
A2 Matemáticas
K1 fuzzy rough set theory
K1 decision rules
K1 classification methods
AB Decision rules are powerful tools to manage information and to provide descriptions of data sets; as a consequence, they can acquire a useful role in decision-making processes where fuzzy rough set theory is applied. This paper focuses on the study of different methods to classify new objects, which are not considered in the starting data set, in order to determine the best possible decision for them. The classification methods are supported by the relevance indicators associated with decision rules, such as support, certainty, and credibility. Specifically, the first one is based on how the new object matches decision rules that describe the data set, while the second one also takes into account the representativeness of these rules. Finally, the third and fourth methods take into account the credibility of the rules compared with the new object. Moreover, we have shown that these methods are richer alternatives or generalize other approaches given in the literature.
PB Multidisciplinary Digital Publishing Institute (MDPI)
SN 2227-7390
YR 2023
FD 2023
LK http://hdl.handle.net/10498/32049
UL http://hdl.handle.net/10498/32049
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026