Evolving Fuzzy Neural Network Based on Uni-nullneuron to Identify Auction Fraud

DOI

10.2991/asum.k.210827.042How to use a DOI?

Keywords

Parallel TEDA, Evolving Fuzzy Neural Network, Uni-Nullnorm, Auction Fraud

Abstract

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, state-of-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.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Paulo Vitor de Campos Souza
AU  - Edwin Lughofer
AU  - Augusto Junio Guimaraes
PY  - 2021
DA  - 2021/08/30
TI  - Evolving Fuzzy Neural Network Based on Uni-nullneuron to Identify Auction Fraud
BT  - 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)
PB  - Atlantis Press
SP  - 314
EP  - 321
SN  - 2589-6644
UR  - https://doi.org/10.2991/asum.k.210827.042
DO  - 10.2991/asum.k.210827.042
ID  - Souza2021
ER  -