Year: 2025
Type: article
Abstract: In data mining and exploration, outliers are specific and infrequent data that require special attention, as they may reveal potentially hazardous information. Detecting outliers can support, e.g., identification fraudulent credit card usage or unauthorized access to transactions, even hacking banking systems, etc. The paper proposes a definition of outlier in terms of fuzzy representations of expert knowledge and its application to detect outliers. The approach proposed has the potential to enhance the performance of outlier detection in various fields, including finance and banking data storage and analysis. By “enhance” we mean that the intention of the new method is to cooperate with known numerical methods, e.g., LOF, rather than supersede or deprecate them. The usefulness of the method is proven via providing new outlying observations for given datasets using input data expressed in an imprecise, linguistic manner.
Source: Applied Sciences
DOI: 10.3390/app152312833
Authors: Marcin Kacprowicz, Adam Niewiadomski
Institution: Lodz University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4416994352