Paulo De Campos Souza, Edwin Lughofer, Augusto Junio Guimarães,
"Evolving Fuzzy Neural Network Based on Uni-nullneuron to Identify Auction Fraud"
: Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP), Serie Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA),, Atlantis Press, Seite(n) 314-321, 9-2021
Original Titel:
Evolving Fuzzy Neural Network Based on Uni-nullneuron to Identify Auction Fraud
Sprache des Titels:
Englisch
Original Buchtitel:
Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP)
Original Kurzfassung:
The increase in transactions on the Internet
related to the purchase of products or services
can provide facilities for the parties involved
in these acquisitions, but they also
generate uncertainties and possibilities of attacks
that can originate from fraud. This
work seeks to explore and extract knowledge
of auction fraud by using an evolving fuzzy
neural network model based on n-uninorms.
This new model uses a fuzzification technique
based on Typicality and Eccentricity
Data Analysis operators and a parallel processor
for stream samples. To test the model
in solving auction fraud problems, stateof-
the-art neuro-fuzzy models were used to
compare a public dataset on the topic. The
results of the model proposed in this paper
were superior to the other models evaluated
(close to 96% accuracy) in the test, and the
fuzzy rules demonstrate the model?s ability
to extract knowledge.
Sprache der Kurzfassung:
Englisch
Veröffentlicher:
Atlantis Press
Serie:
Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA),
Forschungs-