Ant Colony Optimization Algorithm for Feature Selection in Suspicious Transaction Detection System †
Niyazova K. Mukasheva A. Balbayev G. Iliev T. Mirambayeva N. Uzakbayev M.
2024Multidisciplinary Digital Publishing Institute (MDPI)
Engineering Proceedings
2024#60Issue 1
The fight against financial crimes has become increasingly challenging, and the need for sophisticated systems that can accurately identify suspicious transactions has become more pressing. The goal of the study is to develop a new feature selection method based on swarm intelligence algorithms to improve the quality of data classification. This article is about the development of an information system for the classification of transactions into legal and suspicious in an anti-money laundering sphere. The system utilizes a swarm-algorithm-based feature selection approach, specifically the ant colony optimization algorithm, which was both used and adapted for this purpose The article also presents the system’s functional–structural diagram and feature selection algorithm flowchart. The proposed feature selection method can be used to classify data from various subject areas.
AML , ant colony optimization , swarm intelligence
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Department of Information Technology, Non-Profit JSC “Almaty University of Power Engineering and Telecommunications Named after Gumarbek Daukeyev”, Almaty, 050013, Kazakhstan
School of Information Technology and Engineering, Kazakh-British Technical University, Almaty, 050000, Kazakhstan
Academy of Logistics and Transport Almaty, Almaty, 050012, Kazakhstan
Department of Telecommunication, University of Ruse, Ruse, 7004, Bulgaria
Department of Information Technology
School of Information Technology and Engineering
Academy of Logistics and Transport Almaty
Department of Telecommunication
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