Fuzzy Quantifier-Based Fuzzy Rough Sets

In this paper we apply vague quantification to fuzzy rough sets to introduce fuzzy quantifier-based fuzzy rough sets (FQFRS), an intuitive generalization of fuzzy rough sets. We show how several existing models fit in this generalization as well as how it inspires novel models that may improve these...

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Bibliographic Details
Published in2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS) Vol. 30; pp. 269 - 278
Main Authors Theerens, Adnan, Cornelis, Chris
Format Conference Proceeding Journal Article
LanguageEnglish
Published Polish Information Processing Society 01.01.2022
Subjects
Adaptation models
Anomaly detection
Computational efficiency
Computational modeling
Computer science
Robustness
Rough sets
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ISSN2300-5963
DOI10.15439/2022F231

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Summary:In this paper we apply vague quantification to fuzzy rough sets to introduce fuzzy quantifier-based fuzzy rough sets (FQFRS), an intuitive generalization of fuzzy rough sets. We show how several existing models fit in this generalization as well as how it inspires novel models that may improve these existing models. In addition, we introduce several new binary quantification models. Finally, we introduce an adaptation of FQFRS that allows seamless integration of outlier detection algorithms to enhance the robustness of the applications based on FQFRS.
ISSN:2300-5963
DOI:10.15439/2022F231