An Evolving Fuzzy Neural Network Based on Or-Type Logic Neurons for Identifying and Extracting Knowledge in Auction Fraud
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An Evolving Fuzzy Neural Network Based on Or-Type Logic Neurons for Identifying and Extracting Knowledge in Auction Fraud ?
by Paulo Vitor de Campos Souza
1,* [ORCID] , Edwin Lughofer
1 [ORCID] , Huoston Rodrigues Batista
2 [ORCID] and Augusto Junio Guimaraes
3 [ORCID]
1
Institute for Mathematical Methods in Medicine and Data Based Modeling, Johannes Kepler University Linz, 4040 Linz, Austria
2
School of Informatics, Communication and Media, University of Applied Sciences Upper Austria Hagenberg, 4232 Hagenberg im Mühlkreis, Austria
3
Specialization of Artificial Intelligence and Machine Learning, Pontifical Catholic University of Minas Gerais, Belo Horizonte 30535-901, Minas Gerais, Brazil
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Author to whom correspondence should be addressed.
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This paper is an extended version of our paper published in 19th World Congress of the International Fuzzy Systems Association and the 12th Conference of the European Society for Fuzzy Logic and Technology, IFSA-EUSFLAT 2021, Bratislava, Slovakia, 19?24 September 2021; pp. 314?321.
Mathematics 2022, 10(20), 3872; https://doi.org/10.3390/math10203872
Received: 20 September 2022 / Revised: 13 October 2022 / Accepted: 14 October 2022 / Published: 18 October 2022
(This article belongs to the Special Issue Fuzzy Natural Logic in IFSA-EUSFLAT 2021)
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Abstract
The rise in online transactions for purchasing goods and services can benefit the parties involved. However, it also creates uncertainty and the possibility of fraud-related threats. This work aims to explore and extract knowledge of auction fraud by using an innovative evolving fuzzy neural network model based on logic neurons. This model uses a fuzzification technique based on empirical data analysis operators in an evolving way for stream samples. In order to evaluate the applied model, state-of-the-art neuro-fuzzy models were used to compare a public dataset on the topic and, simultaneously, validate the interpretability results based on a common criterion to identify the correct patterns present in the dataset. The fuzzy rules and the interpretability criteria demonstrate the model?s ability to extract knowledge. The results of the model proposed in this paper are superior to the other models evaluated (close to 98.50% accuracy) in the test.
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