Ant Colony Optimization Algorithm for Feature Selection in Suspicious Transaction Detection System
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 i...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/60/1/18 |
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| author | Karina Niyazova Assel Mukasheva Gani Balbayev Teodor Iliev Nazym Mirambayeva Mukhamedali Uzakbayev |
| author_facet | Karina Niyazova Assel Mukasheva Gani Balbayev Teodor Iliev Nazym Mirambayeva Mukhamedali Uzakbayev |
| author_sort | Karina Niyazova |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T18:18:41Z |
| format | Article |
| id | doaj.art-014fc9baad93446a81e641f84583fe63 |
| institution | Directory Open Access Journal |
| issn | 2673-4591 |
| language | English |
| last_indexed | 2024-04-24T18:18:41Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj.art-014fc9baad93446a81e641f84583fe632024-03-27T13:36:43ZengMDPI AGEngineering Proceedings2673-45912024-01-016011810.3390/engproc2024060018Ant Colony Optimization Algorithm for Feature Selection in Suspicious Transaction Detection SystemKarina Niyazova0Assel Mukasheva1Gani Balbayev2Teodor Iliev3Nazym Mirambayeva4Mukhamedali Uzakbayev5Department of Information Technology, Non-Profit JSC “Almaty University of Power Engineering and Telecommunications Named after Gumarbek Daukeyev”, Almaty 050013, KazakhstanSchool of Information Technology and Engineering, Kazakh-British Technical University, Almaty 050000, KazakhstanAcademy of Logistics and Transport Almaty, Almaty 050012, KazakhstanDepartment of Telecommunication, University of Ruse, 7004 Ruse, BulgariaDepartment of Information Technology, Non-Profit JSC “Almaty University of Power Engineering and Telecommunications Named after Gumarbek Daukeyev”, Almaty 050013, KazakhstanDepartment of Information Technology, Non-Profit JSC “Almaty University of Power Engineering and Telecommunications Named after Gumarbek Daukeyev”, Almaty 050013, KazakhstanThe 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.https://www.mdpi.com/2673-4591/60/1/18AMLswarm intelligenceant colony optimization |
| spellingShingle | Karina Niyazova Assel Mukasheva Gani Balbayev Teodor Iliev Nazym Mirambayeva Mukhamedali Uzakbayev Ant Colony Optimization Algorithm for Feature Selection in Suspicious Transaction Detection System Engineering Proceedings AML swarm intelligence ant colony optimization |
| title | Ant Colony Optimization Algorithm for Feature Selection in Suspicious Transaction Detection System |
| title_full | Ant Colony Optimization Algorithm for Feature Selection in Suspicious Transaction Detection System |
| title_fullStr | Ant Colony Optimization Algorithm for Feature Selection in Suspicious Transaction Detection System |
| title_full_unstemmed | Ant Colony Optimization Algorithm for Feature Selection in Suspicious Transaction Detection System |
| title_short | Ant Colony Optimization Algorithm for Feature Selection in Suspicious Transaction Detection System |
| title_sort | ant colony optimization algorithm for feature selection in suspicious transaction detection system |
| topic | AML swarm intelligence ant colony optimization |
| url | https://www.mdpi.com/2673-4591/60/1/18 |
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