2026-05-09T11:16:26.876Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:9279 2024-12-03T20:10:47Z 3

The AdaBoost Approach Tuned by SNS Metaheuristics for Fraud Detection; Chapter in AIS Algorithms for Intelligent Systems: PCCDS 2022: International Conference on Paradigms of Computing, Communication and Data Sciences, Springer 2023 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/9279 https://link.springer.com/chapter/10.1007/978-981-19-8742-7_10 M. Djuric L. Jovanovic M. Zivkovic N. Bacanin M. Antonijevic M. Sarac Recent advances in online payment technologies drastically increased the number of online credit card transactions, which had been additionally fueled by the recent COVID-19 pandemic. Consequently, the number of frauds related to credit cards had also increased drastically, affecting users, merchants, issuing companies, and bank institutions worldwide. One of the crucial tasks is to implement a mechanism that can assure both credit card security and integrity for each online transaction. This paper proposes an adaptive boosting algorithm, that was subjected to the optimization process by the social network search algorithm. The proposed hybrid approach has been validated on the imbalanced synthetic credit card fraud detection benchmark dataset, and acquired outcomes were compared to other cutting-edge machine learning models. The evaluation was performed by utilizing the standard performance indicators—accuracy, recall, precision, Matthews correlation coefficient, and area under the curve. The experimental findings have shown that the proposed SNS-based AdaBoost approach obtained superior results, clearly outperforming all other machine learning models included in the analysis. bookPart Springer, Singapore 115 128 10.1007/978-981-19-8742-7_10 AIS Algorithms for Intelligent Systems: PCCDS 2022: International Conference on Paradigms of Computing, Communication and Data Sciences