Decision Making in Fuzzy Rough Set Theory

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...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-10, Vol.11 (19), p.4187
Main Authors: Chacón-Gómez, Fernando, Cornejo, M. Eugenia, Medina, Jesús
Format: Article
Language:English
Subjects:
Analysis
Approximation
Artificial intelligence
Boolean
Classification
classification methods
Credibility
Datasets
Decision making
decision rules
Decision theory
fuzzy rough set theory
Fuzzy set theory
Fuzzy sets
Information management
Methods
Set theory
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11194187